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Ссылка для ultralytics/models/yolo/world/train_world.py

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ultralytics.models.yolo.world.train_world.WorldTrainerFromScratch

Базы: WorldTrainer

Класс, расширяющий класс WorldTrainer, для обучения модели мира с нуля на открытом наборе данных.

Пример
from ultralytics.models.yolo.world.train_world import WorldTrainerFromScratch
from ultralytics import YOLOWorld

data = dict(
    train=dict(
        yolo_data=["Objects365.yaml"],
        grounding_data=[
            dict(
                img_path="../datasets/flickr30k/images",
                json_file="../datasets/flickr30k/final_flickr_separateGT_train.json",
            ),
            dict(
                img_path="../datasets/GQA/images",
                json_file="../datasets/GQA/final_mixed_train_no_coco.json",
            ),
        ],
    ),
    val=dict(yolo_data=["lvis.yaml"]),
)

model = YOLOWorld("yolov8s-worldv2.yaml")
model.train(data=data, trainer=WorldTrainerFromScratch)
Исходный код в ultralytics/models/yolo/world/train_world.py
class WorldTrainerFromScratch(WorldTrainer):
    """
    A class extending the WorldTrainer class for training a world model from scratch on open-set dataset.

    Example:
        ```python
        from ultralytics.models.yolo.world.train_world import WorldTrainerFromScratch
        from ultralytics import YOLOWorld

        data = dict(
            train=dict(
                yolo_data=["Objects365.yaml"],
                grounding_data=[
                    dict(
                        img_path="../datasets/flickr30k/images",
                        json_file="../datasets/flickr30k/final_flickr_separateGT_train.json",
                    ),
                    dict(
                        img_path="../datasets/GQA/images",
                        json_file="../datasets/GQA/final_mixed_train_no_coco.json",
                    ),
                ],
            ),
            val=dict(yolo_data=["lvis.yaml"]),
        )

        model = YOLOWorld("yolov8s-worldv2.yaml")
        model.train(data=data, trainer=WorldTrainerFromScratch)
        ```
    """

    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
        """Initialize a WorldTrainer object with given arguments."""
        if overrides is None:
            overrides = {}
        super().__init__(cfg, overrides, _callbacks)

    def build_dataset(self, img_path, mode="train", batch=None):
        """
        Build YOLO Dataset.

        Args:
            img_path (List[str] | str): Path to the folder containing images.
            mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode.
            batch (int, optional): Size of batches, this is for `rect`. Defaults to None.
        """
        gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32)
        if mode == "train":
            dataset = [
                build_yolo_dataset(self.args, im_path, batch, self.data, stride=gs, multi_modal=True)
                if isinstance(im_path, str)
                else build_grounding(self.args, im_path["img_path"], im_path["json_file"], batch, stride=gs)
                for im_path in img_path
            ]
            return YOLOConcatDataset(dataset) if len(dataset) > 1 else dataset[0]
        else:
            return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, rect=mode == "val", stride=gs)

    def get_dataset(self):
        """
        Get train, val path from data dict if it exists.

        Returns None if data format is not recognized.
        """
        final_data = dict()
        data_yaml = self.args.data
        assert data_yaml.get("train", False)  # object365.yaml
        assert data_yaml.get("val", False)  # lvis.yaml
        data = {k: [check_det_dataset(d) for d in v.get("yolo_data", [])] for k, v in data_yaml.items()}
        assert len(data["val"]) == 1, f"Only support validating on 1 dataset for now, but got {len(data['val'])}."
        val_split = "minival" if "lvis" in data["val"][0]["val"] else "val"
        for d in data["val"]:
            if d.get("minival") is None:  # for lvis dataset
                continue
            d["minival"] = str(d["path"] / d["minival"])
        for s in ["train", "val"]:
            final_data[s] = [d["train" if s == "train" else val_split] for d in data[s]]
            # save grounding data if there's one
            grounding_data = data_yaml[s].get("grounding_data")
            if grounding_data is None:
                continue
            grounding_data = [grounding_data] if not isinstance(grounding_data, list) else grounding_data
            for g in grounding_data:
                assert isinstance(g, dict), f"Grounding data should be provided in dict format, but got {type(g)}"
            final_data[s] += grounding_data
        # NOTE: to make training work properly, set `nc` and `names`
        final_data["nc"] = data["val"][0]["nc"]
        final_data["names"] = data["val"][0]["names"]
        self.data = final_data
        return final_data["train"], final_data["val"][0]

    def plot_training_labels(self):
        """DO NOT plot labels."""
        pass

    def final_eval(self):
        """Performs final evaluation and validation for object detection YOLO-World model."""
        val = self.args.data["val"]["yolo_data"][0]
        self.validator.args.data = val
        self.validator.args.split = "minival" if isinstance(val, str) and "lvis" in val else "val"
        return super().final_eval()

__init__(cfg=DEFAULT_CFG, overrides=None, _callbacks=None)

Инициализируй объект WorldTrainer с заданными аргументами.

Исходный код в ultralytics/models/yolo/world/train_world.py
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
    """Initialize a WorldTrainer object with given arguments."""
    if overrides is None:
        overrides = {}
    super().__init__(cfg, overrides, _callbacks)

build_dataset(img_path, mode='train', batch=None)

Построй YOLO Dataset.

Параметры:

Имя Тип Описание По умолчанию
img_path List[str] | str

Путь к папке, содержащей изображения.

требуется
mode str

train режим или val Пользователи могут настраивать различные дополнения для каждого режима.

'train'
batch int

Размер партий, это для rect. По умолчанию установлено значение "Нет".

None
Исходный код в ultralytics/models/yolo/world/train_world.py
def build_dataset(self, img_path, mode="train", batch=None):
    """
    Build YOLO Dataset.

    Args:
        img_path (List[str] | str): Path to the folder containing images.
        mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode.
        batch (int, optional): Size of batches, this is for `rect`. Defaults to None.
    """
    gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32)
    if mode == "train":
        dataset = [
            build_yolo_dataset(self.args, im_path, batch, self.data, stride=gs, multi_modal=True)
            if isinstance(im_path, str)
            else build_grounding(self.args, im_path["img_path"], im_path["json_file"], batch, stride=gs)
            for im_path in img_path
        ]
        return YOLOConcatDataset(dataset) if len(dataset) > 1 else dataset[0]
    else:
        return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, rect=mode == "val", stride=gs)

final_eval()

Выполняет финальную оценку и проверку обнаружения объектов YOLO- Модель мира.

Исходный код в ultralytics/models/yolo/world/train_world.py
def final_eval(self):
    """Performs final evaluation and validation for object detection YOLO-World model."""
    val = self.args.data["val"]["yolo_data"][0]
    self.validator.args.data = val
    self.validator.args.split = "minival" if isinstance(val, str) and "lvis" in val else "val"
    return super().final_eval()

get_dataset()

Получи путь train, val из данных dict, если они существуют.

Возвращает None, если формат данных не распознан.

Исходный код в ultralytics/models/yolo/world/train_world.py
def get_dataset(self):
    """
    Get train, val path from data dict if it exists.

    Returns None if data format is not recognized.
    """
    final_data = dict()
    data_yaml = self.args.data
    assert data_yaml.get("train", False)  # object365.yaml
    assert data_yaml.get("val", False)  # lvis.yaml
    data = {k: [check_det_dataset(d) for d in v.get("yolo_data", [])] for k, v in data_yaml.items()}
    assert len(data["val"]) == 1, f"Only support validating on 1 dataset for now, but got {len(data['val'])}."
    val_split = "minival" if "lvis" in data["val"][0]["val"] else "val"
    for d in data["val"]:
        if d.get("minival") is None:  # for lvis dataset
            continue
        d["minival"] = str(d["path"] / d["minival"])
    for s in ["train", "val"]:
        final_data[s] = [d["train" if s == "train" else val_split] for d in data[s]]
        # save grounding data if there's one
        grounding_data = data_yaml[s].get("grounding_data")
        if grounding_data is None:
            continue
        grounding_data = [grounding_data] if not isinstance(grounding_data, list) else grounding_data
        for g in grounding_data:
            assert isinstance(g, dict), f"Grounding data should be provided in dict format, but got {type(g)}"
        final_data[s] += grounding_data
    # NOTE: to make training work properly, set `nc` and `names`
    final_data["nc"] = data["val"][0]["nc"]
    final_data["names"] = data["val"][0]["names"]
    self.data = final_data
    return final_data["train"], final_data["val"][0]

plot_training_labels()

НЕ наклеивай ярлыки.

Исходный код в ultralytics/models/yolo/world/train_world.py
def plot_training_labels(self):
    """DO NOT plot labels."""
    pass





Создано 2024-03-31, Обновлено 2024-03-31
Авторы: Laughing-q (1)