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ultralytics.data.dataset.YOLODataset

Bases : BaseDataset

Classe de jeu de données pour charger les étiquettes de détection et/ou de segmentation d'objets au format YOLO .

Paramètres :

Nom Type Description DĂ©faut
data dict

Un dictionnaire YAML de jeu de données. La valeur par défaut est None.

None
task str

Un arg explicite pour pointer la tâche en cours, La valeur par défaut est 'detect'.

'detect'

Retourne :

Type Description
Dataset

Un objet du jeu de données PyTorch qui peut être utilisé pour former un modèle de détection d'objets.

Code source dans ultralytics/data/dataset.py
class YOLODataset(BaseDataset):
    """
    Dataset class for loading object detection and/or segmentation labels in YOLO format.

    Args:
        data (dict, optional): A dataset YAML dictionary. Defaults to None.
        task (str): An explicit arg to point current task, Defaults to 'detect'.

    Returns:
        (torch.utils.data.Dataset): A PyTorch dataset object that can be used for training an object detection model.
    """

    def __init__(self, *args, data=None, task="detect", **kwargs):
        """Initializes the YOLODataset with optional configurations for segments and keypoints."""
        self.use_segments = task == "segment"
        self.use_keypoints = task == "pose"
        self.use_obb = task == "obb"
        self.data = data
        assert not (self.use_segments and self.use_keypoints), "Can not use both segments and keypoints."
        super().__init__(*args, **kwargs)

    def cache_labels(self, path=Path("./labels.cache")):
        """
        Cache dataset labels, check images and read shapes.

        Args:
            path (Path): Path where to save the cache file. Default is Path('./labels.cache').

        Returns:
            (dict): labels.
        """
        x = {"labels": []}
        nm, nf, ne, nc, msgs = 0, 0, 0, 0, []  # number missing, found, empty, corrupt, messages
        desc = f"{self.prefix}Scanning {path.parent / path.stem}..."
        total = len(self.im_files)
        nkpt, ndim = self.data.get("kpt_shape", (0, 0))
        if self.use_keypoints and (nkpt <= 0 or ndim not in (2, 3)):
            raise ValueError(
                "'kpt_shape' in data.yaml missing or incorrect. Should be a list with [number of "
                "keypoints, number of dims (2 for x,y or 3 for x,y,visible)], i.e. 'kpt_shape: [17, 3]'"
            )
        with ThreadPool(NUM_THREADS) as pool:
            results = pool.imap(
                func=verify_image_label,
                iterable=zip(
                    self.im_files,
                    self.label_files,
                    repeat(self.prefix),
                    repeat(self.use_keypoints),
                    repeat(len(self.data["names"])),
                    repeat(nkpt),
                    repeat(ndim),
                ),
            )
            pbar = TQDM(results, desc=desc, total=total)
            for im_file, lb, shape, segments, keypoint, nm_f, nf_f, ne_f, nc_f, msg in pbar:
                nm += nm_f
                nf += nf_f
                ne += ne_f
                nc += nc_f
                if im_file:
                    x["labels"].append(
                        dict(
                            im_file=im_file,
                            shape=shape,
                            cls=lb[:, 0:1],  # n, 1
                            bboxes=lb[:, 1:],  # n, 4
                            segments=segments,
                            keypoints=keypoint,
                            normalized=True,
                            bbox_format="xywh",
                        )
                    )
                if msg:
                    msgs.append(msg)
                pbar.desc = f"{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt"
            pbar.close()

        if msgs:
            LOGGER.info("\n".join(msgs))
        if nf == 0:
            LOGGER.warning(f"{self.prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}")
        x["hash"] = get_hash(self.label_files + self.im_files)
        x["results"] = nf, nm, ne, nc, len(self.im_files)
        x["msgs"] = msgs  # warnings
        save_dataset_cache_file(self.prefix, path, x)
        return x

    def get_labels(self):
        """Returns dictionary of labels for YOLO training."""
        self.label_files = img2label_paths(self.im_files)
        cache_path = Path(self.label_files[0]).parent.with_suffix(".cache")
        try:
            cache, exists = load_dataset_cache_file(cache_path), True  # attempt to load a *.cache file
            assert cache["version"] == DATASET_CACHE_VERSION  # matches current version
            assert cache["hash"] == get_hash(self.label_files + self.im_files)  # identical hash
        except (FileNotFoundError, AssertionError, AttributeError):
            cache, exists = self.cache_labels(cache_path), False  # run cache ops

        # Display cache
        nf, nm, ne, nc, n = cache.pop("results")  # found, missing, empty, corrupt, total
        if exists and LOCAL_RANK in (-1, 0):
            d = f"Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt"
            TQDM(None, desc=self.prefix + d, total=n, initial=n)  # display results
            if cache["msgs"]:
                LOGGER.info("\n".join(cache["msgs"]))  # display warnings

        # Read cache
        [cache.pop(k) for k in ("hash", "version", "msgs")]  # remove items
        labels = cache["labels"]
        if not labels:
            LOGGER.warning(f"WARNING ⚠️ No images found in {cache_path}, training may not work correctly. {HELP_URL}")
        self.im_files = [lb["im_file"] for lb in labels]  # update im_files

        # Check if the dataset is all boxes or all segments
        lengths = ((len(lb["cls"]), len(lb["bboxes"]), len(lb["segments"])) for lb in labels)
        len_cls, len_boxes, len_segments = (sum(x) for x in zip(*lengths))
        if len_segments and len_boxes != len_segments:
            LOGGER.warning(
                f"WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = {len_segments}, "
                f"len(boxes) = {len_boxes}. To resolve this only boxes will be used and all segments will be removed. "
                "To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset."
            )
            for lb in labels:
                lb["segments"] = []
        if len_cls == 0:
            LOGGER.warning(f"WARNING ⚠️ No labels found in {cache_path}, training may not work correctly. {HELP_URL}")
        return labels

    def build_transforms(self, hyp=None):
        """Builds and appends transforms to the list."""
        if self.augment:
            hyp.mosaic = hyp.mosaic if self.augment and not self.rect else 0.0
            hyp.mixup = hyp.mixup if self.augment and not self.rect else 0.0
            transforms = v8_transforms(self, self.imgsz, hyp)
        else:
            transforms = Compose([LetterBox(new_shape=(self.imgsz, self.imgsz), scaleup=False)])
        transforms.append(
            Format(
                bbox_format="xywh",
                normalize=True,
                return_mask=self.use_segments,
                return_keypoint=self.use_keypoints,
                return_obb=self.use_obb,
                batch_idx=True,
                mask_ratio=hyp.mask_ratio,
                mask_overlap=hyp.overlap_mask,
            )
        )
        return transforms

    def close_mosaic(self, hyp):
        """Sets mosaic, copy_paste and mixup options to 0.0 and builds transformations."""
        hyp.mosaic = 0.0  # set mosaic ratio=0.0
        hyp.copy_paste = 0.0  # keep the same behavior as previous v8 close-mosaic
        hyp.mixup = 0.0  # keep the same behavior as previous v8 close-mosaic
        self.transforms = self.build_transforms(hyp)

    def update_labels_info(self, label):
        """
        Custom your label format here.

        Note:
            cls is not with bboxes now, classification and semantic segmentation need an independent cls label
            Can also support classification and semantic segmentation by adding or removing dict keys there.
        """
        bboxes = label.pop("bboxes")
        segments = label.pop("segments", [])
        keypoints = label.pop("keypoints", None)
        bbox_format = label.pop("bbox_format")
        normalized = label.pop("normalized")

        # NOTE: do NOT resample oriented boxes
        segment_resamples = 100 if self.use_obb else 1000
        if len(segments) > 0:
            # list[np.array(1000, 2)] * num_samples
            # (N, 1000, 2)
            segments = np.stack(resample_segments(segments, n=segment_resamples), axis=0)
        else:
            segments = np.zeros((0, segment_resamples, 2), dtype=np.float32)
        label["instances"] = Instances(bboxes, segments, keypoints, bbox_format=bbox_format, normalized=normalized)
        return label

    @staticmethod
    def collate_fn(batch):
        """Collates data samples into batches."""
        new_batch = {}
        keys = batch[0].keys()
        values = list(zip(*[list(b.values()) for b in batch]))
        for i, k in enumerate(keys):
            value = values[i]
            if k == "img":
                value = torch.stack(value, 0)
            if k in ["masks", "keypoints", "bboxes", "cls", "segments", "obb"]:
                value = torch.cat(value, 0)
            new_batch[k] = value
        new_batch["batch_idx"] = list(new_batch["batch_idx"])
        for i in range(len(new_batch["batch_idx"])):
            new_batch["batch_idx"][i] += i  # add target image index for build_targets()
        new_batch["batch_idx"] = torch.cat(new_batch["batch_idx"], 0)
        return new_batch

__init__(*args, data=None, task='detect', **kwargs)

Initialise le jeu de données YOLOD avec des configurations optionnelles pour les segments et les points clés.

Code source dans ultralytics/data/dataset.py
def __init__(self, *args, data=None, task="detect", **kwargs):
    """Initializes the YOLODataset with optional configurations for segments and keypoints."""
    self.use_segments = task == "segment"
    self.use_keypoints = task == "pose"
    self.use_obb = task == "obb"
    self.data = data
    assert not (self.use_segments and self.use_keypoints), "Can not use both segments and keypoints."
    super().__init__(*args, **kwargs)

build_transforms(hyp=None)

Construit et ajoute des transformations Ă  la liste.

Code source dans ultralytics/data/dataset.py
def build_transforms(self, hyp=None):
    """Builds and appends transforms to the list."""
    if self.augment:
        hyp.mosaic = hyp.mosaic if self.augment and not self.rect else 0.0
        hyp.mixup = hyp.mixup if self.augment and not self.rect else 0.0
        transforms = v8_transforms(self, self.imgsz, hyp)
    else:
        transforms = Compose([LetterBox(new_shape=(self.imgsz, self.imgsz), scaleup=False)])
    transforms.append(
        Format(
            bbox_format="xywh",
            normalize=True,
            return_mask=self.use_segments,
            return_keypoint=self.use_keypoints,
            return_obb=self.use_obb,
            batch_idx=True,
            mask_ratio=hyp.mask_ratio,
            mask_overlap=hyp.overlap_mask,
        )
    )
    return transforms

cache_labels(path=Path('./labels.cache'))

Mets en cache les étiquettes des ensembles de données, vérifie les images et lis les formes.

Paramètres :

Nom Type Description DĂ©faut
path Path

Chemin d'accès où enregistrer le fichier de cache. La valeur par défaut est Path('./labels.cache').

Path('./labels.cache')

Retourne :

Type Description
dict

Ă©tiquettes.

Code source dans ultralytics/data/dataset.py
def cache_labels(self, path=Path("./labels.cache")):
    """
    Cache dataset labels, check images and read shapes.

    Args:
        path (Path): Path where to save the cache file. Default is Path('./labels.cache').

    Returns:
        (dict): labels.
    """
    x = {"labels": []}
    nm, nf, ne, nc, msgs = 0, 0, 0, 0, []  # number missing, found, empty, corrupt, messages
    desc = f"{self.prefix}Scanning {path.parent / path.stem}..."
    total = len(self.im_files)
    nkpt, ndim = self.data.get("kpt_shape", (0, 0))
    if self.use_keypoints and (nkpt <= 0 or ndim not in (2, 3)):
        raise ValueError(
            "'kpt_shape' in data.yaml missing or incorrect. Should be a list with [number of "
            "keypoints, number of dims (2 for x,y or 3 for x,y,visible)], i.e. 'kpt_shape: [17, 3]'"
        )
    with ThreadPool(NUM_THREADS) as pool:
        results = pool.imap(
            func=verify_image_label,
            iterable=zip(
                self.im_files,
                self.label_files,
                repeat(self.prefix),
                repeat(self.use_keypoints),
                repeat(len(self.data["names"])),
                repeat(nkpt),
                repeat(ndim),
            ),
        )
        pbar = TQDM(results, desc=desc, total=total)
        for im_file, lb, shape, segments, keypoint, nm_f, nf_f, ne_f, nc_f, msg in pbar:
            nm += nm_f
            nf += nf_f
            ne += ne_f
            nc += nc_f
            if im_file:
                x["labels"].append(
                    dict(
                        im_file=im_file,
                        shape=shape,
                        cls=lb[:, 0:1],  # n, 1
                        bboxes=lb[:, 1:],  # n, 4
                        segments=segments,
                        keypoints=keypoint,
                        normalized=True,
                        bbox_format="xywh",
                    )
                )
            if msg:
                msgs.append(msg)
            pbar.desc = f"{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt"
        pbar.close()

    if msgs:
        LOGGER.info("\n".join(msgs))
    if nf == 0:
        LOGGER.warning(f"{self.prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}")
    x["hash"] = get_hash(self.label_files + self.im_files)
    x["results"] = nf, nm, ne, nc, len(self.im_files)
    x["msgs"] = msgs  # warnings
    save_dataset_cache_file(self.prefix, path, x)
    return x

close_mosaic(hyp)

Fixe les options mosaïque, copier_coller et mélange à 0.0 et construit les transformations.

Code source dans ultralytics/data/dataset.py
def close_mosaic(self, hyp):
    """Sets mosaic, copy_paste and mixup options to 0.0 and builds transformations."""
    hyp.mosaic = 0.0  # set mosaic ratio=0.0
    hyp.copy_paste = 0.0  # keep the same behavior as previous v8 close-mosaic
    hyp.mixup = 0.0  # keep the same behavior as previous v8 close-mosaic
    self.transforms = self.build_transforms(hyp)

collate_fn(batch) staticmethod

Rassemble les échantillons de données en lots.

Code source dans ultralytics/data/dataset.py
@staticmethod
def collate_fn(batch):
    """Collates data samples into batches."""
    new_batch = {}
    keys = batch[0].keys()
    values = list(zip(*[list(b.values()) for b in batch]))
    for i, k in enumerate(keys):
        value = values[i]
        if k == "img":
            value = torch.stack(value, 0)
        if k in ["masks", "keypoints", "bboxes", "cls", "segments", "obb"]:
            value = torch.cat(value, 0)
        new_batch[k] = value
    new_batch["batch_idx"] = list(new_batch["batch_idx"])
    for i in range(len(new_batch["batch_idx"])):
        new_batch["batch_idx"][i] += i  # add target image index for build_targets()
    new_batch["batch_idx"] = torch.cat(new_batch["batch_idx"], 0)
    return new_batch

get_labels()

Renvoie le dictionnaire des Ă©tiquettes pour la formation YOLO .

Code source dans ultralytics/data/dataset.py
def get_labels(self):
    """Returns dictionary of labels for YOLO training."""
    self.label_files = img2label_paths(self.im_files)
    cache_path = Path(self.label_files[0]).parent.with_suffix(".cache")
    try:
        cache, exists = load_dataset_cache_file(cache_path), True  # attempt to load a *.cache file
        assert cache["version"] == DATASET_CACHE_VERSION  # matches current version
        assert cache["hash"] == get_hash(self.label_files + self.im_files)  # identical hash
    except (FileNotFoundError, AssertionError, AttributeError):
        cache, exists = self.cache_labels(cache_path), False  # run cache ops

    # Display cache
    nf, nm, ne, nc, n = cache.pop("results")  # found, missing, empty, corrupt, total
    if exists and LOCAL_RANK in (-1, 0):
        d = f"Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt"
        TQDM(None, desc=self.prefix + d, total=n, initial=n)  # display results
        if cache["msgs"]:
            LOGGER.info("\n".join(cache["msgs"]))  # display warnings

    # Read cache
    [cache.pop(k) for k in ("hash", "version", "msgs")]  # remove items
    labels = cache["labels"]
    if not labels:
        LOGGER.warning(f"WARNING ⚠️ No images found in {cache_path}, training may not work correctly. {HELP_URL}")
    self.im_files = [lb["im_file"] for lb in labels]  # update im_files

    # Check if the dataset is all boxes or all segments
    lengths = ((len(lb["cls"]), len(lb["bboxes"]), len(lb["segments"])) for lb in labels)
    len_cls, len_boxes, len_segments = (sum(x) for x in zip(*lengths))
    if len_segments and len_boxes != len_segments:
        LOGGER.warning(
            f"WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = {len_segments}, "
            f"len(boxes) = {len_boxes}. To resolve this only boxes will be used and all segments will be removed. "
            "To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset."
        )
        for lb in labels:
            lb["segments"] = []
    if len_cls == 0:
        LOGGER.warning(f"WARNING ⚠️ No labels found in {cache_path}, training may not work correctly. {HELP_URL}")
    return labels

update_labels_info(label)

Personnalise le format de ton Ă©tiquette ici.

Note

cls n'est pas avec les bboxes maintenant, la classification et la segmentation sémantique ont besoin d'une étiquette cls indépendante. Peut également prendre en charge la classification et la segmentation sémantique en ajoutant ou en supprimant des clés dict à cet endroit.

Code source dans ultralytics/data/dataset.py
def update_labels_info(self, label):
    """
    Custom your label format here.

    Note:
        cls is not with bboxes now, classification and semantic segmentation need an independent cls label
        Can also support classification and semantic segmentation by adding or removing dict keys there.
    """
    bboxes = label.pop("bboxes")
    segments = label.pop("segments", [])
    keypoints = label.pop("keypoints", None)
    bbox_format = label.pop("bbox_format")
    normalized = label.pop("normalized")

    # NOTE: do NOT resample oriented boxes
    segment_resamples = 100 if self.use_obb else 1000
    if len(segments) > 0:
        # list[np.array(1000, 2)] * num_samples
        # (N, 1000, 2)
        segments = np.stack(resample_segments(segments, n=segment_resamples), axis=0)
    else:
        segments = np.zeros((0, segment_resamples, 2), dtype=np.float32)
    label["instances"] = Instances(bboxes, segments, keypoints, bbox_format=bbox_format, normalized=normalized)
    return label



ultralytics.data.dataset.ClassificationDataset

Bases : ImageFolder

Étend torchvision ImageFolder pour prendre en charge les tâches de classification YOLO , en offrant des fonctionnalités telles que l'augmentation de l'image, la mise en cache et la vérification. d'images, la mise en cache et la vérification. Il est conçu pour traiter efficacement de grands ensembles de données pour la formation de modèles d'apprentissage profond. modèles d'apprentissage profond, avec des transformations d'images et des mécanismes de mise en cache optionnels pour accélérer la formation.

Cette classe permet des augmentations à l'aide des bibliothèques torchvision et Albumentations, et prend en charge la mise en cache des images dans la RAM ou sur le disque pour réduire la charge d'entrée-sortie. dans la RAM ou sur le disque pour réduire la charge d'E/S pendant la formation. En outre, elle met en œuvre un processus de vérification robuste pour garantir l'intégrité et la cohérence des données.

Attributs :

Nom Type Description
cache_ram bool

Indique si la mise en cache dans la mémoire vive est activée.

cache_disk bool

Indique si la mise en cache sur disque est activée.

samples list

Une liste de tuples, chacun contenant le chemin d'accès à une image, son index de classe, le chemin d'accès à son fichier cache .npy (si la mise en cache se fait sur disque), et éventuellement le tableau d'images chargées (si la mise en cache se fait en RAM).

torch_transforms callable

PyTorch les transformations Ă  appliquer aux images.

Code source dans ultralytics/data/dataset.py
class ClassificationDataset(torchvision.datasets.ImageFolder):
    """
    Extends torchvision ImageFolder to support YOLO classification tasks, offering functionalities like image
    augmentation, caching, and verification. It's designed to efficiently handle large datasets for training deep
    learning models, with optional image transformations and caching mechanisms to speed up training.

    This class allows for augmentations using both torchvision and Albumentations libraries, and supports caching images
    in RAM or on disk to reduce IO overhead during training. Additionally, it implements a robust verification process
    to ensure data integrity and consistency.

    Attributes:
        cache_ram (bool): Indicates if caching in RAM is enabled.
        cache_disk (bool): Indicates if caching on disk is enabled.
        samples (list): A list of tuples, each containing the path to an image, its class index, path to its .npy cache
                        file (if caching on disk), and optionally the loaded image array (if caching in RAM).
        torch_transforms (callable): PyTorch transforms to be applied to the images.
    """

    def __init__(self, root, args, augment=False, prefix=""):
        """
        Initialize YOLO object with root, image size, augmentations, and cache settings.

        Args:
            root (str): Path to the dataset directory where images are stored in a class-specific folder structure.
            args (Namespace): Configuration containing dataset-related settings such as image size, augmentation
                parameters, and cache settings. It includes attributes like `imgsz` (image size), `fraction` (fraction
                of data to use), `scale`, `fliplr`, `flipud`, `cache` (disk or RAM caching for faster training),
                `auto_augment`, `hsv_h`, `hsv_s`, `hsv_v`, and `crop_fraction`.
            augment (bool, optional): Whether to apply augmentations to the dataset. Default is False.
            prefix (str, optional): Prefix for logging and cache filenames, aiding in dataset identification and
                debugging. Default is an empty string.
        """
        super().__init__(root=root)
        if augment and args.fraction < 1.0:  # reduce training fraction
            self.samples = self.samples[: round(len(self.samples) * args.fraction)]
        self.prefix = colorstr(f"{prefix}: ") if prefix else ""
        self.cache_ram = args.cache is True or args.cache == "ram"  # cache images into RAM
        self.cache_disk = args.cache == "disk"  # cache images on hard drive as uncompressed *.npy files
        self.samples = self.verify_images()  # filter out bad images
        self.samples = [list(x) + [Path(x[0]).with_suffix(".npy"), None] for x in self.samples]  # file, index, npy, im
        scale = (1.0 - args.scale, 1.0)  # (0.08, 1.0)
        self.torch_transforms = (
            classify_augmentations(
                size=args.imgsz,
                scale=scale,
                hflip=args.fliplr,
                vflip=args.flipud,
                erasing=args.erasing,
                auto_augment=args.auto_augment,
                hsv_h=args.hsv_h,
                hsv_s=args.hsv_s,
                hsv_v=args.hsv_v,
            )
            if augment
            else classify_transforms(size=args.imgsz, crop_fraction=args.crop_fraction)
        )

    def __getitem__(self, i):
        """Returns subset of data and targets corresponding to given indices."""
        f, j, fn, im = self.samples[i]  # filename, index, filename.with_suffix('.npy'), image
        if self.cache_ram and im is None:
            im = self.samples[i][3] = cv2.imread(f)
        elif self.cache_disk:
            if not fn.exists():  # load npy
                np.save(fn.as_posix(), cv2.imread(f), allow_pickle=False)
            im = np.load(fn)
        else:  # read image
            im = cv2.imread(f)  # BGR
        # Convert NumPy array to PIL image
        im = Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))
        sample = self.torch_transforms(im)
        return {"img": sample, "cls": j}

    def __len__(self) -> int:
        """Return the total number of samples in the dataset."""
        return len(self.samples)

    def verify_images(self):
        """Verify all images in dataset."""
        desc = f"{self.prefix}Scanning {self.root}..."
        path = Path(self.root).with_suffix(".cache")  # *.cache file path

        with contextlib.suppress(FileNotFoundError, AssertionError, AttributeError):
            cache = load_dataset_cache_file(path)  # attempt to load a *.cache file
            assert cache["version"] == DATASET_CACHE_VERSION  # matches current version
            assert cache["hash"] == get_hash([x[0] for x in self.samples])  # identical hash
            nf, nc, n, samples = cache.pop("results")  # found, missing, empty, corrupt, total
            if LOCAL_RANK in (-1, 0):
                d = f"{desc} {nf} images, {nc} corrupt"
                TQDM(None, desc=d, total=n, initial=n)
                if cache["msgs"]:
                    LOGGER.info("\n".join(cache["msgs"]))  # display warnings
            return samples

        # Run scan if *.cache retrieval failed
        nf, nc, msgs, samples, x = 0, 0, [], [], {}
        with ThreadPool(NUM_THREADS) as pool:
            results = pool.imap(func=verify_image, iterable=zip(self.samples, repeat(self.prefix)))
            pbar = TQDM(results, desc=desc, total=len(self.samples))
            for sample, nf_f, nc_f, msg in pbar:
                if nf_f:
                    samples.append(sample)
                if msg:
                    msgs.append(msg)
                nf += nf_f
                nc += nc_f
                pbar.desc = f"{desc} {nf} images, {nc} corrupt"
            pbar.close()
        if msgs:
            LOGGER.info("\n".join(msgs))
        x["hash"] = get_hash([x[0] for x in self.samples])
        x["results"] = nf, nc, len(samples), samples
        x["msgs"] = msgs  # warnings
        save_dataset_cache_file(self.prefix, path, x)
        return samples

__getitem__(i)

Renvoie le sous-ensemble de données et de cibles correspondant aux indices donnés.

Code source dans ultralytics/data/dataset.py
def __getitem__(self, i):
    """Returns subset of data and targets corresponding to given indices."""
    f, j, fn, im = self.samples[i]  # filename, index, filename.with_suffix('.npy'), image
    if self.cache_ram and im is None:
        im = self.samples[i][3] = cv2.imread(f)
    elif self.cache_disk:
        if not fn.exists():  # load npy
            np.save(fn.as_posix(), cv2.imread(f), allow_pickle=False)
        im = np.load(fn)
    else:  # read image
        im = cv2.imread(f)  # BGR
    # Convert NumPy array to PIL image
    im = Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))
    sample = self.torch_transforms(im)
    return {"img": sample, "cls": j}

__init__(root, args, augment=False, prefix='')

Initialise l'objet YOLO avec la racine, la taille de l'image, les augmentations et les paramètres du cache.

Paramètres :

Nom Type Description DĂ©faut
root str

Chemin d'accès au répertoire du jeu de données où les images sont stockées dans une structure de dossiers spécifique à la classe.

requis
args Namespace

Configuration contenant les paramètres liés au jeu de données, tels que la taille de l'image, les paramètres d'augmentation et les paramètres du cache. les paramètres d'augmentation et les paramètres de cache. Elle comprend des attributs tels que imgsz (taille de l'image), fraction (fraction des données à utiliser), scale, fliplr, flipud, cache (mise en cache du disque ou de la RAM pour une formation plus rapide), auto_augment, hsv_h, hsv_s, hsv_vet crop_fraction.

requis
augment bool

Indique s'il faut appliquer des augmentations à l'ensemble de données. La valeur par défaut est False.

False
prefix str

Préfixe pour les noms de fichiers d'enregistrement et de cache, facilitant l'identification des ensembles de données et le débogage. débogage. La valeur par défaut est une chaîne vide.

''
Code source dans ultralytics/data/dataset.py
def __init__(self, root, args, augment=False, prefix=""):
    """
    Initialize YOLO object with root, image size, augmentations, and cache settings.

    Args:
        root (str): Path to the dataset directory where images are stored in a class-specific folder structure.
        args (Namespace): Configuration containing dataset-related settings such as image size, augmentation
            parameters, and cache settings. It includes attributes like `imgsz` (image size), `fraction` (fraction
            of data to use), `scale`, `fliplr`, `flipud`, `cache` (disk or RAM caching for faster training),
            `auto_augment`, `hsv_h`, `hsv_s`, `hsv_v`, and `crop_fraction`.
        augment (bool, optional): Whether to apply augmentations to the dataset. Default is False.
        prefix (str, optional): Prefix for logging and cache filenames, aiding in dataset identification and
            debugging. Default is an empty string.
    """
    super().__init__(root=root)
    if augment and args.fraction < 1.0:  # reduce training fraction
        self.samples = self.samples[: round(len(self.samples) * args.fraction)]
    self.prefix = colorstr(f"{prefix}: ") if prefix else ""
    self.cache_ram = args.cache is True or args.cache == "ram"  # cache images into RAM
    self.cache_disk = args.cache == "disk"  # cache images on hard drive as uncompressed *.npy files
    self.samples = self.verify_images()  # filter out bad images
    self.samples = [list(x) + [Path(x[0]).with_suffix(".npy"), None] for x in self.samples]  # file, index, npy, im
    scale = (1.0 - args.scale, 1.0)  # (0.08, 1.0)
    self.torch_transforms = (
        classify_augmentations(
            size=args.imgsz,
            scale=scale,
            hflip=args.fliplr,
            vflip=args.flipud,
            erasing=args.erasing,
            auto_augment=args.auto_augment,
            hsv_h=args.hsv_h,
            hsv_s=args.hsv_s,
            hsv_v=args.hsv_v,
        )
        if augment
        else classify_transforms(size=args.imgsz, crop_fraction=args.crop_fraction)
    )

__len__()

Renvoie le nombre total d'échantillons dans l'ensemble de données.

Code source dans ultralytics/data/dataset.py
def __len__(self) -> int:
    """Return the total number of samples in the dataset."""
    return len(self.samples)

verify_images()

Vérifie toutes les images de l'ensemble de données.

Code source dans ultralytics/data/dataset.py
def verify_images(self):
    """Verify all images in dataset."""
    desc = f"{self.prefix}Scanning {self.root}..."
    path = Path(self.root).with_suffix(".cache")  # *.cache file path

    with contextlib.suppress(FileNotFoundError, AssertionError, AttributeError):
        cache = load_dataset_cache_file(path)  # attempt to load a *.cache file
        assert cache["version"] == DATASET_CACHE_VERSION  # matches current version
        assert cache["hash"] == get_hash([x[0] for x in self.samples])  # identical hash
        nf, nc, n, samples = cache.pop("results")  # found, missing, empty, corrupt, total
        if LOCAL_RANK in (-1, 0):
            d = f"{desc} {nf} images, {nc} corrupt"
            TQDM(None, desc=d, total=n, initial=n)
            if cache["msgs"]:
                LOGGER.info("\n".join(cache["msgs"]))  # display warnings
        return samples

    # Run scan if *.cache retrieval failed
    nf, nc, msgs, samples, x = 0, 0, [], [], {}
    with ThreadPool(NUM_THREADS) as pool:
        results = pool.imap(func=verify_image, iterable=zip(self.samples, repeat(self.prefix)))
        pbar = TQDM(results, desc=desc, total=len(self.samples))
        for sample, nf_f, nc_f, msg in pbar:
            if nf_f:
                samples.append(sample)
            if msg:
                msgs.append(msg)
            nf += nf_f
            nc += nc_f
            pbar.desc = f"{desc} {nf} images, {nc} corrupt"
        pbar.close()
    if msgs:
        LOGGER.info("\n".join(msgs))
    x["hash"] = get_hash([x[0] for x in self.samples])
    x["results"] = nf, nc, len(samples), samples
    x["msgs"] = msgs  # warnings
    save_dataset_cache_file(self.prefix, path, x)
    return samples



ultralytics.data.dataset.SemanticDataset

Bases : BaseDataset

Ensemble de données de segmentation sémantique.

Cette classe est chargée de gérer les ensembles de données utilisés pour les tâches de segmentation sémantique. Elle hérite des fonctionnalités de la classe BaseDataset.

Note

Cette classe est actuellement un substitut et doit être complétée par des méthodes et des attributs pour soutenir les tâches de segmentation sémantique. les tâches de segmentation sémantique.

Code source dans ultralytics/data/dataset.py
class SemanticDataset(BaseDataset):
    """
    Semantic Segmentation Dataset.

    This class is responsible for handling datasets used for semantic segmentation tasks. It inherits functionalities
    from the BaseDataset class.

    Note:
        This class is currently a placeholder and needs to be populated with methods and attributes for supporting
        semantic segmentation tasks.
    """

    def __init__(self):
        """Initialize a SemanticDataset object."""
        super().__init__()

__init__()

Initialise un objet SemanticDataset.

Code source dans ultralytics/data/dataset.py
def __init__(self):
    """Initialize a SemanticDataset object."""
    super().__init__()



ultralytics.data.dataset.load_dataset_cache_file(path)

Charge un dictionnaire Ultralytics *.cache Ă  partir du chemin.

Code source dans ultralytics/data/dataset.py
def load_dataset_cache_file(path):
    """Load an Ultralytics *.cache dictionary from path."""
    import gc

    gc.disable()  # reduce pickle load time https://github.com/ultralytics/ultralytics/pull/1585
    cache = np.load(str(path), allow_pickle=True).item()  # load dict
    gc.enable()
    return cache



ultralytics.data.dataset.save_dataset_cache_file(prefix, path, x)

Sauvegarde un ensemble de données Ultralytics *.cache dictionary x dans le chemin d'accès.

Code source dans ultralytics/data/dataset.py
def save_dataset_cache_file(prefix, path, x):
    """Save an Ultralytics dataset *.cache dictionary x to path."""
    x["version"] = DATASET_CACHE_VERSION  # add cache version
    if is_dir_writeable(path.parent):
        if path.exists():
            path.unlink()  # remove *.cache file if exists
        np.save(str(path), x)  # save cache for next time
        path.with_suffix(".cache.npy").rename(path)  # remove .npy suffix
        LOGGER.info(f"{prefix}New cache created: {path}")
    else:
        LOGGER.warning(f"{prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable, cache not saved.")





Créé le 2023-11-12, Mis à jour le 2023-11-25
Auteurs : glenn-jocher (3), Laughing-q (1)