─░├žeri─če ge├ž

Referans i├žin ultralytics/data/build.py

Not

Bu dosya https://github.com/ultralytics/ultralytics/blob/main/ ultralytics/data/build .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.data.build.InfiniteDataLoader

├ťsler: DataLoader

├çal─▒┼čanlar─▒ yeniden kullanan veri y├╝kleyici.

Vanilla DataLoader ile ayn─▒ s├Âzdizimini kullan─▒r.

Kaynak kodu ultralytics/data/build.py
class InfiniteDataLoader(dataloader.DataLoader):
    """
    Dataloader that reuses workers.

    Uses same syntax as vanilla DataLoader.
    """

    def __init__(self, *args, **kwargs):
        """Dataloader that infinitely recycles workers, inherits from DataLoader."""
        super().__init__(*args, **kwargs)
        object.__setattr__(self, "batch_sampler", _RepeatSampler(self.batch_sampler))
        self.iterator = super().__iter__()

    def __len__(self):
        """Returns the length of the batch sampler's sampler."""
        return len(self.batch_sampler.sampler)

    def __iter__(self):
        """Creates a sampler that repeats indefinitely."""
        for _ in range(len(self)):
            yield next(self.iterator)

    def reset(self):
        """
        Reset iterator.

        This is useful when we want to modify settings of dataset while training.
        """
        self.iterator = self._get_iterator()

__init__(*args, **kwargs)

─░┼č├žileri sonsuza kadar geri d├Ân├╝┼čt├╝ren Dataloader, DataLoader'dan miras al─▒r.

Kaynak kodu ultralytics/data/build.py
def __init__(self, *args, **kwargs):
    """Dataloader that infinitely recycles workers, inherits from DataLoader."""
    super().__init__(*args, **kwargs)
    object.__setattr__(self, "batch_sampler", _RepeatSampler(self.batch_sampler))
    self.iterator = super().__iter__()

__iter__()

S├╝resiz olarak tekrar eden bir ├Ârnekleyici olu┼čturur.

Kaynak kodu ultralytics/data/build.py
def __iter__(self):
    """Creates a sampler that repeats indefinitely."""
    for _ in range(len(self)):
        yield next(self.iterator)

__len__()

Toplu ├Ârnekleyicinin ├Ârnekleyicisinin uzunlu─čunu d├Ând├╝r├╝r.

Kaynak kodu ultralytics/data/build.py
def __len__(self):
    """Returns the length of the batch sampler's sampler."""
    return len(self.batch_sampler.sampler)

reset()

Yineleyiciyi s─▒f─▒rla.

Bu, e─čitim s─▒ras─▒nda veri k├╝mesinin ayarlar─▒n─▒ de─či┼čtirmek istedi─čimizde kullan─▒┼čl─▒d─▒r.

Kaynak kodu ultralytics/data/build.py
def reset(self):
    """
    Reset iterator.

    This is useful when we want to modify settings of dataset while training.
    """
    self.iterator = self._get_iterator()



ultralytics.data.build._RepeatSampler

Sonsuza kadar tekrar eden ├Ârnekleyici.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
sampler sampler

Tekrarlanacak ├Ârnekleyici.

gerekli
Kaynak kodu ultralytics/data/build.py
class _RepeatSampler:
    """
    Sampler that repeats forever.

    Args:
        sampler (Dataset.sampler): The sampler to repeat.
    """

    def __init__(self, sampler):
        """Initializes an object that repeats a given sampler indefinitely."""
        self.sampler = sampler

    def __iter__(self):
        """Iterates over the 'sampler' and yields its contents."""
        while True:
            yield from iter(self.sampler)

__init__(sampler)

Belirli bir ├Ârnekleyiciyi s├╝resiz olarak tekrarlayan bir nesneyi ba┼člat─▒r.

Kaynak kodu ultralytics/data/build.py
def __init__(self, sampler):
    """Initializes an object that repeats a given sampler indefinitely."""
    self.sampler = sampler

__iter__()

'├ľrnekleyici' ├╝zerinde yineleme yapar ve i├žeri─čini verir.

Kaynak kodu ultralytics/data/build.py
def __iter__(self):
    """Iterates over the 'sampler' and yields its contents."""
    while True:
        yield from iter(self.sampler)



ultralytics.data.build.seed_worker(worker_id)

Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader.

Kaynak kodu ultralytics/data/build.py
def seed_worker(worker_id):  # noqa
    """Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader."""
    worker_seed = torch.initial_seed() % 2**32
    np.random.seed(worker_seed)
    random.seed(worker_seed)



ultralytics.data.build.build_yolo_dataset(cfg, img_path, batch, data, mode='train', rect=False, stride=32, multi_modal=False)

YOLO Veri K├╝mesini Olu┼čturun.

Kaynak kodu ultralytics/data/build.py
def build_yolo_dataset(cfg, img_path, batch, data, mode="train", rect=False, stride=32, multi_modal=False):
    """Build YOLO Dataset."""
    dataset = YOLOMultiModalDataset if multi_modal else YOLODataset
    return dataset(
        img_path=img_path,
        imgsz=cfg.imgsz,
        batch_size=batch,
        augment=mode == "train",  # augmentation
        hyp=cfg,  # TODO: probably add a get_hyps_from_cfg function
        rect=cfg.rect or rect,  # rectangular batches
        cache=cfg.cache or None,
        single_cls=cfg.single_cls or False,
        stride=int(stride),
        pad=0.0 if mode == "train" else 0.5,
        prefix=colorstr(f"{mode}: "),
        task=cfg.task,
        classes=cfg.classes,
        data=data,
        fraction=cfg.fraction if mode == "train" else 1.0,
    )



ultralytics.data.build.build_grounding(cfg, img_path, json_file, batch, mode='train', rect=False, stride=32)

YOLO Veri K├╝mesini Olu┼čturun.

Kaynak kodu ultralytics/data/build.py
def build_grounding(cfg, img_path, json_file, batch, mode="train", rect=False, stride=32):
    """Build YOLO Dataset."""
    return GroundingDataset(
        img_path=img_path,
        json_file=json_file,
        imgsz=cfg.imgsz,
        batch_size=batch,
        augment=mode == "train",  # augmentation
        hyp=cfg,  # TODO: probably add a get_hyps_from_cfg function
        rect=cfg.rect or rect,  # rectangular batches
        cache=cfg.cache or None,
        single_cls=cfg.single_cls or False,
        stride=int(stride),
        pad=0.0 if mode == "train" else 0.5,
        prefix=colorstr(f"{mode}: "),
        task=cfg.task,
        classes=cfg.classes,
        fraction=cfg.fraction if mode == "train" else 1.0,
    )



ultralytics.data.build.build_dataloader(dataset, batch, workers, shuffle=True, rank=-1)

E─čitim veya do─črulama k├╝mesi i├žin bir InfiniteDataLoader veya DataLoader d├Ând├╝r├╝r.

Kaynak kodu ultralytics/data/build.py
def build_dataloader(dataset, batch, workers, shuffle=True, rank=-1):
    """Return an InfiniteDataLoader or DataLoader for training or validation set."""
    batch = min(batch, len(dataset))
    nd = torch.cuda.device_count()  # number of CUDA devices
    nw = min(os.cpu_count() // max(nd, 1), workers)  # number of workers
    sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
    generator = torch.Generator()
    generator.manual_seed(6148914691236517205 + RANK)
    return InfiniteDataLoader(
        dataset=dataset,
        batch_size=batch,
        shuffle=shuffle and sampler is None,
        num_workers=nw,
        sampler=sampler,
        pin_memory=PIN_MEMORY,
        collate_fn=getattr(dataset, "collate_fn", None),
        worker_init_fn=seed_worker,
        generator=generator,
    )



ultralytics.data.build.check_source(source)

Kaynak t├╝r├╝n├╝ kontrol edin ve ilgili bayrak de─čerlerini d├Ând├╝r├╝n.

Kaynak kodu ultralytics/data/build.py
def check_source(source):
    """Check source type and return corresponding flag values."""
    webcam, screenshot, from_img, in_memory, tensor = False, False, False, False, False
    if isinstance(source, (str, int, Path)):  # int for local usb camera
        source = str(source)
        is_file = Path(source).suffix[1:] in (IMG_FORMATS | VID_FORMATS)
        is_url = source.lower().startswith(("https://", "http://", "rtsp://", "rtmp://", "tcp://"))
        webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file)
        screenshot = source.lower() == "screen"
        if is_url and is_file:
            source = check_file(source)  # download
    elif isinstance(source, LOADERS):
        in_memory = True
    elif isinstance(source, (list, tuple)):
        source = autocast_list(source)  # convert all list elements to PIL or np arrays
        from_img = True
    elif isinstance(source, (Image.Image, np.ndarray)):
        from_img = True
    elif isinstance(source, torch.Tensor):
        tensor = True
    else:
        raise TypeError("Unsupported image type. For supported types see https://docs.ultralytics.com/modes/predict")

    return source, webcam, screenshot, from_img, in_memory, tensor



ultralytics.data.build.load_inference_source(source=None, batch=1, vid_stride=1, buffer=False)

Nesne alg─▒lama i├žin bir ├ž─▒kar─▒m kayna─č─▒ y├╝kler ve gerekli d├Ân├╝┼č├╝mleri uygular.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
source (str, Path, Tensor, Image, ndarray)

├ç─▒kar─▒m i├žin girdi kayna─č─▒.

None
batch int

Veri y├╝kleyicileri i├žin toplu i┼č boyutu. Varsay─▒lan de─čer 1'dir.

1
vid_stride int

Video kaynaklar─▒ i├žin kare aral─▒─č─▒. Varsay─▒lan de─čer 1'dir.

1
buffer bool

Ak─▒┼č ├žer├ževelerinin tamponlan─▒p tamponlanmayaca─č─▒n─▒ belirler. Varsay─▒lan de─čer False'dir.

False

─░ade:

─░sim Tip A├ž─▒klama
dataset Dataset

Belirtilen giri┼č kayna─č─▒ i├žin bir veri k├╝mesi nesnesi.

Kaynak kodu ultralytics/data/build.py
def load_inference_source(source=None, batch=1, vid_stride=1, buffer=False):
    """
    Loads an inference source for object detection and applies necessary transformations.

    Args:
        source (str, Path, Tensor, PIL.Image, np.ndarray): The input source for inference.
        batch (int, optional): Batch size for dataloaders. Default is 1.
        vid_stride (int, optional): The frame interval for video sources. Default is 1.
        buffer (bool, optional): Determined whether stream frames will be buffered. Default is False.

    Returns:
        dataset (Dataset): A dataset object for the specified input source.
    """
    source, stream, screenshot, from_img, in_memory, tensor = check_source(source)
    source_type = source.source_type if in_memory else SourceTypes(stream, screenshot, from_img, tensor)

    # Dataloader
    if tensor:
        dataset = LoadTensor(source)
    elif in_memory:
        dataset = source
    elif stream:
        dataset = LoadStreams(source, vid_stride=vid_stride, buffer=buffer)
    elif screenshot:
        dataset = LoadScreenshots(source)
    elif from_img:
        dataset = LoadPilAndNumpy(source)
    else:
        dataset = LoadImagesAndVideos(source, batch=batch, vid_stride=vid_stride)

    # Attach source types to the dataset
    setattr(dataset, "source_type", source_type)

    return dataset





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