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Référence pour ultralytics/utils/autobatch.py

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ultralytics.utils.autobatch.check_train_batch_size(model, imgsz=640, amp=True, batch=-1)

Compute optimal YOLO training batch size using the autobatch() function.

Paramètres :

Nom Type Description DĂ©faut
model Module

YOLO modèle pour vérifier la taille du lot.

requis
imgsz int

Taille de l'image utilisée pour la formation.

640
amp bool

Si True, utilise la précision mixte automatique (AMP) pour la formation.

True

Retourne :

Type Description
int

Taille optimale du lot calculée à l'aide de la fonction autobatch().

Code source dans ultralytics/utils/autobatch.py
def check_train_batch_size(model, imgsz=640, amp=True, batch=-1):
    """
    Compute optimal YOLO training batch size using the autobatch() function.

    Args:
        model (torch.nn.Module): YOLO model to check batch size for.
        imgsz (int): Image size used for training.
        amp (bool): If True, use automatic mixed precision (AMP) for training.

    Returns:
        (int): Optimal batch size computed using the autobatch() function.
    """

    with torch.cuda.amp.autocast(amp):
        return autobatch(deepcopy(model).train(), imgsz, fraction=batch if 0.0 < batch < 1.0 else 0.6)



ultralytics.utils.autobatch.autobatch(model, imgsz=640, fraction=0.6, batch_size=DEFAULT_CFG.batch)

Estime automatiquement la meilleure taille de lot YOLO pour utiliser une fraction de la mémoire CUDA disponible.

Paramètres :

Nom Type Description DĂ©faut
model module

YOLO pour calculer la taille du lot.

requis
imgsz int

La taille de l'image utilisée comme entrée pour le modèle YOLO . La valeur par défaut est 640.

640
fraction float

La fraction de la mémoire CUDA disponible à utiliser. La valeur par défaut est 0,60.

0.6
batch_size int

La taille du lot par défaut à utiliser si une erreur est détectée. La valeur par défaut est 16.

batch

Retourne :

Type Description
int

La taille optimale des lots.

Code source dans ultralytics/utils/autobatch.py
def autobatch(model, imgsz=640, fraction=0.60, batch_size=DEFAULT_CFG.batch):
    """
    Automatically estimate the best YOLO batch size to use a fraction of the available CUDA memory.

    Args:
        model (torch.nn.module): YOLO model to compute batch size for.
        imgsz (int, optional): The image size used as input for the YOLO model. Defaults to 640.
        fraction (float, optional): The fraction of available CUDA memory to use. Defaults to 0.60.
        batch_size (int, optional): The default batch size to use if an error is detected. Defaults to 16.

    Returns:
        (int): The optimal batch size.
    """

    # Check device
    prefix = colorstr("AutoBatch: ")
    LOGGER.info(f"{prefix}Computing optimal batch size for imgsz={imgsz} at {fraction * 100}% CUDA memory utilization.")
    device = next(model.parameters()).device  # get model device
    if device.type in {"cpu", "mps"}:
        LOGGER.info(f"{prefix} ⚠️ intended for CUDA devices, using default batch-size {batch_size}")
        return batch_size
    if torch.backends.cudnn.benchmark:
        LOGGER.info(f"{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}")
        return batch_size

    # Inspect CUDA memory
    gb = 1 << 30  # bytes to GiB (1024 ** 3)
    d = str(device).upper()  # 'CUDA:0'
    properties = torch.cuda.get_device_properties(device)  # device properties
    t = properties.total_memory / gb  # GiB total
    r = torch.cuda.memory_reserved(device) / gb  # GiB reserved
    a = torch.cuda.memory_allocated(device) / gb  # GiB allocated
    f = t - (r + a)  # GiB free
    LOGGER.info(f"{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free")

    # Profile batch sizes
    batch_sizes = [1, 2, 4, 8, 16]
    try:
        img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes]
        results = profile(img, model, n=3, device=device)

        # Fit a solution
        y = [x[2] for x in results if x]  # memory [2]
        p = np.polyfit(batch_sizes[: len(y)], y, deg=1)  # first degree polynomial fit
        b = int((f * fraction - p[1]) / p[0])  # y intercept (optimal batch size)
        if None in results:  # some sizes failed
            i = results.index(None)  # first fail index
            if b >= batch_sizes[i]:  # y intercept above failure point
                b = batch_sizes[max(i - 1, 0)]  # select prior safe point
        if b < 1 or b > 1024:  # b outside of safe range
            b = batch_size
            LOGGER.info(f"{prefix}WARNING ⚠️ CUDA anomaly detected, using default batch-size {batch_size}.")

        fraction = (np.polyval(p, b) + r + a) / t  # actual fraction predicted
        LOGGER.info(f"{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) âś…")
        return b
    except Exception as e:
        LOGGER.warning(f"{prefix}WARNING ⚠️ error detected: {e},  using default batch-size {batch_size}.")
        return batch_size





Created 2023-11-12, Updated 2024-06-02
Authors: glenn-jocher (5), Burhan-Q (1), Laughing-q (1)