83 lines
3.4 KiB
Python
83 lines
3.4 KiB
Python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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from __future__ import annotations
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from copy import copy
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from pathlib import Path
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from typing import Any
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from ultralytics.models import yolo
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from ultralytics.nn.tasks import OBBModel
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from ultralytics.utils import DEFAULT_CFG, RANK
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class OBBTrainer(yolo.detect.DetectionTrainer):
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"""
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A class extending the DetectionTrainer class for training based on an Oriented Bounding Box (OBB) model.
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This trainer specializes in training YOLO models that detect oriented bounding boxes, which are useful for
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detecting objects at arbitrary angles rather than just axis-aligned rectangles.
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Attributes:
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loss_names (tuple): Names of the loss components used during training including box_loss, cls_loss,
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and dfl_loss.
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Methods:
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get_model: Return OBBModel initialized with specified config and weights.
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get_validator: Return an instance of OBBValidator for validation of YOLO model.
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Examples:
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>>> from ultralytics.models.yolo.obb import OBBTrainer
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>>> args = dict(model="yolo11n-obb.pt", data="dota8.yaml", epochs=3)
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>>> trainer = OBBTrainer(overrides=args)
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>>> trainer.train()
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"""
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def __init__(self, cfg=DEFAULT_CFG, overrides: dict | None = None, _callbacks: list[Any] | None = None):
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"""
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Initialize an OBBTrainer object for training Oriented Bounding Box (OBB) models.
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Args:
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cfg (dict, optional): Configuration dictionary for the trainer. Contains training parameters and
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model configuration.
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overrides (dict, optional): Dictionary of parameter overrides for the configuration. Any values here
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will take precedence over those in cfg.
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_callbacks (list[Any], optional): List of callback functions to be invoked during training.
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"""
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if overrides is None:
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overrides = {}
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overrides["task"] = "obb"
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super().__init__(cfg, overrides, _callbacks)
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def get_model(
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self, cfg: str | dict | None = None, weights: str | Path | None = None, verbose: bool = True
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) -> OBBModel:
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"""
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Return OBBModel initialized with specified config and weights.
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Args:
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cfg (str | dict, optional): Model configuration. Can be a path to a YAML config file, a dictionary
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containing configuration parameters, or None to use default configuration.
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weights (str | Path, optional): Path to pretrained weights file. If None, random initialization is used.
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verbose (bool): Whether to display model information during initialization.
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Returns:
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(OBBModel): Initialized OBBModel with the specified configuration and weights.
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Examples:
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>>> trainer = OBBTrainer()
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>>> model = trainer.get_model(cfg="yolo11n-obb.yaml", weights="yolo11n-obb.pt")
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"""
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model = OBBModel(cfg, nc=self.data["nc"], ch=self.data["channels"], verbose=verbose and RANK == -1)
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if weights:
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model.load(weights)
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return model
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def get_validator(self):
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"""Return an instance of OBBValidator for validation of YOLO model."""
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self.loss_names = "box_loss", "cls_loss", "dfl_loss"
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return yolo.obb.OBBValidator(
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self.test_loader, save_dir=self.save_dir, args=copy(self.args), _callbacks=self.callbacks
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)
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