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507 lines
21 KiB
507 lines
21 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license
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import sys
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from pathlib import Path
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from typing import Union
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from ultralytics import yolo # noqa
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from ultralytics.nn.tasks import (ClassificationModel, DetectionModel, PoseModel, SegmentationModel,
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attempt_load_one_weight, guess_model_task, nn, yaml_model_load)
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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, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, ROOT, callbacks,
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is_git_dir, yaml_load)
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from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_pip_update_available, 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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TASK_MAP = {
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'classify': [
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ClassificationModel, yolo.v8.classify.ClassificationTrainer, yolo.v8.classify.ClassificationValidator,
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yolo.v8.classify.ClassificationPredictor],
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'detect': [
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DetectionModel, yolo.v8.detect.DetectionTrainer, yolo.v8.detect.DetectionValidator,
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yolo.v8.detect.DetectionPredictor],
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'segment': [
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SegmentationModel, yolo.v8.segment.SegmentationTrainer, yolo.v8.segment.SegmentationValidator,
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yolo.v8.segment.SegmentationPredictor],
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'pose': [PoseModel, yolo.v8.pose.PoseTrainer, yolo.v8.pose.PoseValidator, yolo.v8.pose.PosePredictor]}
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class YOLO:
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"""
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YOLO (You Only Look Once) object detection model.
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Args:
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model (str, Path): Path to the model file to load or create.
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task (Any, optional): Task type for the YOLO model. Defaults to None.
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Attributes:
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predictor (Any): The predictor object.
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model (Any): The model object.
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trainer (Any): The trainer object.
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task (str): The type of model task.
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ckpt (Any): The checkpoint object if the model loaded from *.pt file.
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cfg (str): The model configuration if loaded from *.yaml file.
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ckpt_path (str): The checkpoint file path.
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overrides (dict): Overrides for the trainer object.
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metrics (Any): The data for metrics.
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Methods:
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__call__(source=None, stream=False, **kwargs):
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Alias for the predict method.
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_new(cfg:str, verbose:bool=True) -> None:
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Initializes a new model and infers the task type from the model definitions.
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_load(weights:str, task:str='') -> None:
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Initializes a new model and infers the task type from the model head.
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_check_is_pytorch_model() -> None:
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Raises TypeError if the model is not a PyTorch model.
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reset() -> None:
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Resets the model modules.
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info(verbose:bool=False) -> None:
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Logs the model info.
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fuse() -> None:
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Fuses the model for faster inference.
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predict(source=None, stream=False, **kwargs) -> List[ultralytics.yolo.engine.results.Results]:
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Performs prediction using the YOLO model.
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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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def __init__(self, model: Union[str, Path] = 'yolov8n.pt', task=None) -> None:
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"""
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Initializes the YOLO model.
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Args:
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model (Union[str, Path], optional): Path or name of the model to load or create. Defaults to 'yolov8n.pt'.
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task (Any, optional): Task type for the YOLO model. Defaults to None.
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"""
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self.callbacks = callbacks.get_default_callbacks()
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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 = None # validation/training metrics
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self.session = None # HUB session
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model = str(model).strip() # strip spaces
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# Check if Ultralytics HUB model from https://hub.ultralytics.com
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if self.is_hub_model(model):
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from ultralytics.hub.session import HUBTrainingSession
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self.session = HUBTrainingSession(model)
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model = self.session.model_file
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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, task)
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else:
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self._load(model, task)
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def __call__(self, source=None, stream=False, **kwargs):
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"""Calls the 'predict' function with given arguments to perform object detection."""
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return self.predict(source, stream, **kwargs)
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def __getattr__(self, attr):
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"""Raises error if object has no requested attribute."""
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name = self.__class__.__name__
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raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
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@staticmethod
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def is_hub_model(model):
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"""Check if the provided model is a HUB model."""
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return any((
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model.startswith('https://hub.ultra'), # i.e. https://hub.ultralytics.com/models/MODEL_ID
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[len(x) for x in model.split('_')] == [42, 20], # APIKEY_MODELID
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len(model) == 20 and not Path(model).exists() and all(x not in model for x in './\\'))) # MODELID
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def _new(self, cfg: str, task=None, 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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task (str) or (None): model task
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verbose (bool): display model info on load
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"""
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cfg_dict = yaml_model_load(cfg)
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self.cfg = cfg
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self.task = task or guess_model_task(cfg_dict)
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self.model = TASK_MAP[self.task][0](cfg_dict, verbose=verbose and RANK == -1) # build model
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self.overrides['model'] = self.cfg
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# Below added to allow export from yamls
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args = {**DEFAULT_CFG_DICT, **self.overrides} # combine model and default args, preferring model args
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self.model.args = {k: v for k, v in args.items() if k in DEFAULT_CFG_KEYS} # attach args to model
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self.model.task = self.task
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def _load(self, weights: str, task=None):
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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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task (str) or (None): model task
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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 = self._reset_ckpt_args(self.model.args)
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self.ckpt_path = self.model.pt_path
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else:
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weights = check_file(weights)
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self.model, self.ckpt = weights, None
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self.task = task or 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.overrides['task'] = self.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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pt_str = isinstance(self.model, (str, Path)) and Path(self.model).suffix == '.pt'
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pt_module = isinstance(self.model, nn.Module)
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if not (pt_module or pt_str):
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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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@smart_inference_mode()
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def reset_weights(self):
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"""
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Resets the model modules parameters to randomly initialized values, losing all training information.
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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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return self
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@smart_inference_mode()
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def load(self, weights='yolov8n.pt'):
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"""
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Transfers parameters with matching names and shapes from 'weights' to model.
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"""
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self._check_is_pytorch_model()
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if isinstance(weights, (str, Path)):
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weights, self.ckpt = attempt_load_one_weight(weights)
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self.model.load(weights)
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return self
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def info(self, verbose=True):
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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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"""Fuse PyTorch Conv2d and BatchNorm2d layers."""
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self._check_is_pytorch_model()
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self.model.fuse()
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@smart_inference_mode()
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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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if source is None:
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source = ROOT / 'assets' if is_git_dir() else 'https://ultralytics.com/images/bus.jpg'
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LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using 'source={source}'.")
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is_cli = (sys.argv[0].endswith('yolo') or sys.argv[0].endswith('ultralytics')) and any(
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x in sys.argv for x in ('predict', 'track', 'mode=predict', 'mode=track'))
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overrides = self.overrides.copy()
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overrides['conf'] = 0.25
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overrides.update(kwargs) # prefer 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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if not is_cli:
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overrides['save'] = kwargs.get('save', False) # do not save by default if called in Python
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if not self.predictor:
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self.task = overrides.get('task') or self.task
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self.predictor = TASK_MAP[self.task][3](overrides=overrides, _callbacks=self.callbacks)
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self.predictor.setup_model(model=self.model, verbose=is_cli)
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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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return self.predictor.predict_cli(source=source) if is_cli else self.predictor(source=source, stream=stream)
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def track(self, source=None, stream=False, persist=False, **kwargs):
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"""
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Perform object tracking on the input source using the registered trackers.
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Args:
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source (str, optional): The input source for object tracking. Can be a file path or a video stream.
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stream (bool, optional): Whether the input source is a video stream. Defaults to False.
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persist (bool, optional): Whether to persist the trackers if they already exist. Defaults to False.
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**kwargs (optional): Additional keyword arguments for the tracking process.
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Returns:
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(List[ultralytics.yolo.engine.results.Results]): The tracking results.
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"""
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if not hasattr(self.predictor, 'trackers'):
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from ultralytics.tracker import register_tracker
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register_tracker(self, persist)
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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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if 'task' in overrides:
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self.task = args.task
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else:
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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 = TASK_MAP[self.task][2](args=args, _callbacks=self.callbacks)
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validator(model=self.model)
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self.metrics = validator.metrics
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return validator.metrics
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@smart_inference_mode()
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def benchmark(self, **kwargs):
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"""
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Benchmark a model on all export formats.
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Args:
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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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self._check_is_pytorch_model()
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from ultralytics.yolo.utils.benchmarks import benchmark
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overrides = self.model.args.copy()
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overrides.update(kwargs)
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overrides['mode'] = 'benchmark'
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overrides = {**DEFAULT_CFG_DICT, **overrides} # fill in missing overrides keys with defaults
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return benchmark(model=self, imgsz=overrides['imgsz'], half=overrides['half'], device=overrides['device'])
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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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overrides['mode'] = 'export'
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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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return Exporter(overrides=args, _callbacks=self.callbacks)(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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if self.session: # Ultralytics HUB session
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if any(kwargs):
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LOGGER.warning('WARNING ⚠️ using HUB training arguments, ignoring local training arguments.')
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kwargs = self.session.train_args
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check_pip_update_available()
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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']))
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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.task = overrides.get('task') or self.task
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self.trainer = TASK_MAP[self.task][1](overrides=overrides, _callbacks=self.callbacks)
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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.hub_session = self.session # attach optional HUB session
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self.trainer.train()
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# Update model and cfg after training
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if RANK in (-1, 0):
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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 = 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 tune(self,
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data: str,
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space: dict = None,
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grace_period: int = 10,
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gpu_per_trial: int = None,
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max_samples: int = 10,
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train_args: dict = {}):
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"""
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Runs hyperparameter tuning using Ray Tune.
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Args:
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data (str): The dataset to run the tuner on.
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space (dict, optional): The hyperparameter search space. Defaults to None.
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grace_period (int, optional): The grace period in epochs of the ASHA scheduler. Defaults to 10.
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gpu_per_trial (int, optional): The number of GPUs to allocate per trial. Defaults to None.
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max_samples (int, optional): The maximum number of trials to run. Defaults to 10.
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train_args (dict, optional): Additional arguments to pass to the `train()` method. Defaults to {}.
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Returns:
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(dict): A dictionary containing the results of the hyperparameter search.
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Raises:
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ModuleNotFoundError: If Ray Tune is not installed.
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"""
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try:
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from ultralytics.yolo.utils.tuner import (ASHAScheduler, RunConfig, WandbLoggerCallback, default_space,
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task_metric_map, tune)
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except ImportError:
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raise ModuleNotFoundError("Install Ray Tune: `pip install 'ray[tune]'`")
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try:
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import wandb
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from wandb import __version__ # noqa
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except ImportError:
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wandb = False
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def _tune(config):
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"""
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Trains the YOLO model with the specified hyperparameters and additional arguments.
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Args:
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config (dict): A dictionary of hyperparameters to use for training.
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Returns:
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None.
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"""
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self._reset_callbacks()
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config.update(train_args)
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self.train(**config)
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if not space:
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LOGGER.warning('WARNING: search space not provided. Using default search space')
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space = default_space
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space['data'] = data
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# Define the trainable function with allocated resources
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trainable_with_resources = tune.with_resources(_tune, {'cpu': 8, 'gpu': gpu_per_trial if gpu_per_trial else 0})
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# Define the ASHA scheduler for hyperparameter search
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asha_scheduler = ASHAScheduler(time_attr='epoch',
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metric=task_metric_map[self.task],
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mode='max',
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max_t=train_args.get('epochs') or 100,
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grace_period=grace_period,
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reduction_factor=3)
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|
|
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# Define the callbacks for the hyperparameter search
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|
tuner_callbacks = [WandbLoggerCallback(project='yolov8_tune') if wandb else None]
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|
|
|
# Create the Ray Tune hyperparameter search tuner
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|
tuner = tune.Tuner(trainable_with_resources,
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param_space=space,
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tune_config=tune.TuneConfig(scheduler=asha_scheduler, num_samples=max_samples),
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run_config=RunConfig(callbacks=tuner_callbacks, local_dir='./runs'))
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|
|
|
# Run the hyperparameter search
|
|
tuner.fit()
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|
|
|
# Return the results of the hyperparameter search
|
|
return tuner.get_results()
|
|
|
|
@property
|
|
def names(self):
|
|
"""
|
|
Returns class names of the loaded model.
|
|
"""
|
|
return self.model.names if hasattr(self.model, 'names') else None
|
|
|
|
@property
|
|
def device(self):
|
|
"""
|
|
Returns device if PyTorch model
|
|
"""
|
|
return next(self.model.parameters()).device if isinstance(self.model, nn.Module) else None
|
|
|
|
@property
|
|
def transforms(self):
|
|
"""
|
|
Returns transform of the loaded model.
|
|
"""
|
|
return self.model.transforms if hasattr(self.model, 'transforms') else None
|
|
|
|
def add_callback(self, event: str, func):
|
|
"""
|
|
Add callback
|
|
"""
|
|
self.callbacks[event].append(func)
|
|
|
|
@staticmethod
|
|
def _reset_ckpt_args(args):
|
|
"""Reset arguments when loading a PyTorch model."""
|
|
include = {'imgsz', 'data', 'task', 'single_cls'} # only remember these arguments when loading a PyTorch model
|
|
return {k: v for k, v in args.items() if k in include}
|
|
|
|
def _reset_callbacks(self):
|
|
"""Reset all registered callbacks."""
|
|
for event in callbacks.default_callbacks.keys():
|
|
self.callbacks[event] = [callbacks.default_callbacks[event][0]]
|