138 lines
5.3 KiB
Python
138 lines
5.3 KiB
Python
# Ultralytics YOLO 🚀, AGPL-3.0 license
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from pathlib import Path
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from ultralytics.engine.model import Model
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from ultralytics.models import yolo
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from ultralytics.nn.tasks import ClassificationModel, DetectionModel, DetectionBinModel, DetrackModel, DemaskModel, DetrackmaskModel, DetectionQGModel, OBBModel, PoseModel, SegmentationModel, WorldModel
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from ultralytics.utils import ROOT, yaml_load
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class YOLO(Model):
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"""YOLO (You Only Look Once) object detection model."""
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def __init__(self, model="yolov8n.pt", task=None, verbose=False):
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"""Initialize YOLO model, switching to YOLOWorld if model filename contains '-world'."""
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path = Path(model)
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if "-world" in path.stem and path.suffix in {".pt", ".yaml", ".yml"}: # if YOLOWorld PyTorch model
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new_instance = YOLOWorld(path, verbose=verbose)
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self.__class__ = type(new_instance)
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self.__dict__ = new_instance.__dict__
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else:
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# Continue with default YOLO initialization
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super().__init__(model=model, task=task, verbose=verbose)
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@property
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def task_map(self):
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"""Map head to model, trainer, validator, and predictor classes."""
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return {
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"classify": {
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"model": ClassificationModel,
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"trainer": yolo.classify.ClassificationTrainer,
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"validator": yolo.classify.ClassificationValidator,
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"predictor": yolo.classify.ClassificationPredictor,
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},
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"detect": {
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"model": DetectionModel,
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"trainer": yolo.detect.DetectionTrainer,
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"validator": yolo.detect.DetectionValidator,
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"predictor": yolo.detect.DetectionPredictor,
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},
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"detectbin": {
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"model": DetectionBinModel,
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"trainer": yolo.detectbin.DetectionTrainer,
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"validator": yolo.detectbin.DetectionValidator,
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"predictor": yolo.detectbin.DetectionPredictor,
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},
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"detectqg": {
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"model": DetectionQGModel,
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"trainer": yolo.detectqg.DetectionTrainer,
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"validator": yolo.detectqg.DetectionValidator,
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"predictor": yolo.detectqg.DetectionPredictor,
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},
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"detrack": {
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"model": DetrackModel,
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"trainer": yolo.detrack.DetectionTrainer,
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"validator": yolo.detrack.DetectionValidator,
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"predictor": yolo.detrack.DetectionPredictor,
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},
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"demask": {
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"model": DemaskModel,
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"trainer": yolo.demask.DetectionTrainer,
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"validator": yolo.demask.DetectionValidator,
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"predictor": yolo.demask.DetectionPredictor,
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},
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"detrackmask": {
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"model": DetrackmaskModel,
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"trainer": yolo.detrackmask.DetectionTrainer,
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"validator": yolo.detrackmask.DetectionValidator,
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"predictor": yolo.detrackmask.DetectionPredictor,
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},
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"segment": {
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"model": SegmentationModel,
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"trainer": yolo.segment.SegmentationTrainer,
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"validator": yolo.segment.SegmentationValidator,
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"predictor": yolo.segment.SegmentationPredictor,
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},
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"pose": {
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"model": PoseModel,
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"trainer": yolo.pose.PoseTrainer,
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"validator": yolo.pose.PoseValidator,
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"predictor": yolo.pose.PosePredictor,
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},
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"obb": {
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"model": OBBModel,
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"trainer": yolo.obb.OBBTrainer,
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"validator": yolo.obb.OBBValidator,
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"predictor": yolo.obb.OBBPredictor,
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},
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}
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class YOLOWorld(Model):
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"""YOLO-World object detection model."""
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def __init__(self, model="yolov8s-world.pt", verbose=False) -> None:
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"""
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Initializes the YOLOv8-World model with the given pre-trained model file. Supports *.pt and *.yaml formats.
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Args:
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model (str | Path): Path to the pre-trained model. Defaults to 'yolov8s-world.pt'.
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"""
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super().__init__(model=model, task="detect", verbose=verbose)
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# Assign default COCO class names when there are no custom names
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if not hasattr(self.model, "names"):
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self.model.names = yaml_load(ROOT / "cfg/datasets/coco8.yaml").get("names")
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@property
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def task_map(self):
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"""Map head to model, validator, and predictor classes."""
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return {
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"detect": {
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"model": WorldModel,
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"validator": yolo.detect.DetectionValidator,
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"predictor": yolo.detect.DetectionPredictor,
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"trainer": yolo.world.WorldTrainer,
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}
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}
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def set_classes(self, classes):
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"""
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Set classes.
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Args:
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classes (List(str)): A list of categories i.e ["person"].
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"""
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self.model.set_classes(classes)
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# Remove background if it's given
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background = " "
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if background in classes:
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classes.remove(background)
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self.model.names = classes
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# Reset method class names
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# self.predictor = None # reset predictor otherwise old names remain
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if self.predictor:
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self.predictor.model.names = classes
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