─░├žeri─če ge├ž

Referans i├žin ultralytics/utils/autobatch.py

Not

Bu dosya https://github.com/ultralytics/ultralytics/blob/main/ ultralytics/utils/autobatch .py adresinde mevcuttur. Bir sorun tespit ederseniz l├╝tfen bir ├çekme ─░ste─či ­čŤá´ŞĆ ile katk─▒da bulunarak d├╝zeltilmesine yard─▒mc─▒ olun. Te┼čekk├╝rler ­čÖĆ!



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

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

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
model Module

YOLO i├žin parti boyutunu kontrol etmek i├žin model.

gerekli
imgsz int

E─čitim i├žin kullan─▒lan g├Âr├╝nt├╝ boyutu.

640
amp bool

True ise, e─čitim i├žin otomatik kar─▒┼č─▒k hassasiyet (AMP) kullan─▒n.

True

─░ade:

Tip A├ž─▒klama
int

autobatch() i┼člevi kullan─▒larak hesaplanan optimum parti boyutu.

Kaynak kodu 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)

Mevcut CUDA belle─činin bir k─▒sm─▒n─▒ kullanmak i├žin en iyi YOLO y─▒─č─▒n boyutunu otomatik olarak tahmin edin.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
model module

YOLO i├žin parti boyutunu hesaplamak i├žin model.

gerekli
imgsz int

YOLO modeli i├žin girdi olarak kullan─▒lan g├Âr├╝nt├╝ boyutu. Varsay─▒lan de─čer 640't─▒r.

640
fraction float

Kullan─▒labilir CUDA belle─činin kullan─▒lacak k─▒sm─▒. Varsay─▒lan de─čer 0,60't─▒r.

0.6
batch_size int

Bir hata alg─▒land─▒─č─▒nda kullan─▒lacak varsay─▒lan parti boyutu. Varsay─▒lan de─čer 16'd─▒r.

batch

─░ade:

Tip A├ž─▒klama
int

Optimum parti b├╝y├╝kl├╝─č├╝.

Kaynak kodu 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 == "cpu":
        LOGGER.info(f"{prefix}CUDA not detected, using default CPU batch-size {batch_size}")
        return batch_size
    if torch.backends.cudnn.benchmark:
        LOGGER.info(f"{prefix} ÔÜá´ŞĆ 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 ÔÜá´ŞĆ 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}%) Ôťů")
        return b
    except Exception as e:
        LOGGER.warning(f"{prefix}WARNING ÔÜá´ŞĆ 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)