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Riferimento per ultralytics/data/dataset.py

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ultralytics.data.dataset.YOLODataset

Basi: BaseDataset

Classe di dati per caricare le etichette di rilevamento e/o segmentazione degli oggetti in formato YOLO .

Parametri:

Nome Tipo Descrizione Predefinito
data dict

Un dizionario YAML del dataset. Il valore predefinito è Nessuno.

None
task str

Un argomento esplicito per puntare l'attività corrente, predefinito a 'detect'.

'detect'

Restituzione:

Tipo Descrizione
Dataset

Un oggetto del dataset PyTorch che può essere utilizzato per addestrare un modello di rilevamento degli oggetti.

Codice sorgente in 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(
                        {
                            "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, DATASET_CACHE_VERSION)
        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,
                bgr=hyp.bgr if self.augment else 0.0,  # only affect training.
            )
        )
        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)

Inizializza lo YOLODataset con le configurazioni opzionali per i segmenti e i punti chiave.

Codice sorgente in 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)

Costruisce e aggiunge le trasformazioni all'elenco.

Codice sorgente in 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,
            bgr=hyp.bgr if self.augment else 0.0,  # only affect training.
        )
    )
    return transforms

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

Memorizza le etichette del dataset, controlla le immagini e legge le forme.

Parametri:

Nome Tipo Descrizione Predefinito
path Path

Percorso in cui salvare il file della cache. Il valore predefinito è Path('./labels.cache').

Path('./labels.cache')

Restituzione:

Tipo Descrizione
dict

etichette.

Codice sorgente in 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(
                    {
                        "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, DATASET_CACHE_VERSION)
    return x

close_mosaic(hyp)

Imposta le opzioni mosaico, copia_incolla e mixup su 0.0 e costruisce le trasformazioni.

Codice sorgente in 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

Raccoglie i campioni di dati in lotti.

Codice sorgente in 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()

Restituisce il dizionario delle etichette per la formazione di YOLO .

Codice sorgente in 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)

Personalizza il formato della tua etichetta qui.

Nota

cls non è con le bbox ora, la classificazione e la segmentazione semantica hanno bisogno di un'etichetta cls indipendente. Può anche supportare la classificazione e la segmentazione semantica aggiungendo o rimuovendo le chiavi dict.

Codice sorgente in 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.YOLOMultiModalDataset

Basi: YOLODataset

Classe di dati per caricare le etichette di rilevamento e/o segmentazione degli oggetti in formato YOLO .

Parametri:

Nome Tipo Descrizione Predefinito
data dict

Un dizionario YAML del dataset. Il valore predefinito è Nessuno.

None
task str

Un argomento esplicito per puntare l'attività corrente, predefinito a 'detect'.

'detect'

Restituzione:

Tipo Descrizione
Dataset

Un oggetto del dataset PyTorch che può essere utilizzato per addestrare un modello di rilevamento degli oggetti.

Codice sorgente in ultralytics/data/dataset.py
class YOLOMultiModalDataset(YOLODataset):
    """
    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 a dataset object for object detection tasks with optional specifications."""
        super().__init__(*args, data=data, task=task, **kwargs)

    def update_labels_info(self, label):
        """Add texts information for multi modal model training."""
        labels = super().update_labels_info(label)
        # NOTE: some categories are concatenated with its synonyms by `/`.
        labels["texts"] = [v.split("/") for _, v in self.data["names"].items()]
        return labels

    def build_transforms(self, hyp=None):
        """Enhances data transformations with optional text augmentation for multi-modal training."""
        transforms = super().build_transforms(hyp)
        if self.augment:
            # NOTE: hard-coded the args for now.
            transforms.insert(-1, RandomLoadText(max_samples=min(self.data["nc"], 80), padding=True))
        return transforms

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

Inizializza un oggetto dataset per le attività di rilevamento degli oggetti con specifiche opzionali.

Codice sorgente in ultralytics/data/dataset.py
def __init__(self, *args, data=None, task="detect", **kwargs):
    """Initializes a dataset object for object detection tasks with optional specifications."""
    super().__init__(*args, data=data, task=task, **kwargs)

build_transforms(hyp=None)

Migliora le trasformazioni dei dati con l'aggiunta di testo opzionale per la formazione multimodale.

Codice sorgente in ultralytics/data/dataset.py
def build_transforms(self, hyp=None):
    """Enhances data transformations with optional text augmentation for multi-modal training."""
    transforms = super().build_transforms(hyp)
    if self.augment:
        # NOTE: hard-coded the args for now.
        transforms.insert(-1, RandomLoadText(max_samples=min(self.data["nc"], 80), padding=True))
    return transforms

update_labels_info(label)

Aggiungi informazioni sui testi per la formazione di modelli multimodali.

Codice sorgente in ultralytics/data/dataset.py
def update_labels_info(self, label):
    """Add texts information for multi modal model training."""
    labels = super().update_labels_info(label)
    # NOTE: some categories are concatenated with its synonyms by `/`.
    labels["texts"] = [v.split("/") for _, v in self.data["names"].items()]
    return labels



ultralytics.data.dataset.GroundingDataset

Basi: YOLODataset

Codice sorgente in ultralytics/data/dataset.py
class GroundingDataset(YOLODataset):
    def __init__(self, *args, task="detect", json_file, **kwargs):
        """Initializes a GroundingDataset for object detection, loading annotations from a specified JSON file."""
        assert task == "detect", "`GroundingDataset` only support `detect` task for now!"
        self.json_file = json_file
        super().__init__(*args, task=task, data={}, **kwargs)

    def get_img_files(self, img_path):
        """The image files would be read in `get_labels` function, return empty list here."""
        return []

    def get_labels(self):
        """Loads annotations from a JSON file, filters, and normalizes bounding boxes for each image."""
        labels = []
        LOGGER.info("Loading annotation file...")
        with open(self.json_file, "r") as f:
            annotations = json.load(f)
        images = {f'{x["id"]:d}': x for x in annotations["images"]}
        imgToAnns = defaultdict(list)
        for ann in annotations["annotations"]:
            imgToAnns[ann["image_id"]].append(ann)
        for img_id, anns in TQDM(imgToAnns.items(), desc=f"Reading annotations {self.json_file}"):
            img = images[f"{img_id:d}"]
            h, w, f = img["height"], img["width"], img["file_name"]
            im_file = Path(self.img_path) / f
            if not im_file.exists():
                continue
            self.im_files.append(str(im_file))
            bboxes = []
            cat2id = {}
            texts = []
            for ann in anns:
                if ann["iscrowd"]:
                    continue
                box = np.array(ann["bbox"], dtype=np.float32)
                box[:2] += box[2:] / 2
                box[[0, 2]] /= float(w)
                box[[1, 3]] /= float(h)
                if box[2] <= 0 or box[3] <= 0:
                    continue

                cat_name = " ".join([img["caption"][t[0] : t[1]] for t in ann["tokens_positive"]])
                if cat_name not in cat2id:
                    cat2id[cat_name] = len(cat2id)
                    texts.append([cat_name])
                cls = cat2id[cat_name]  # class
                box = [cls] + box.tolist()
                if box not in bboxes:
                    bboxes.append(box)
            lb = np.array(bboxes, dtype=np.float32) if len(bboxes) else np.zeros((0, 5), dtype=np.float32)
            labels.append(
                {
                    "im_file": im_file,
                    "shape": (h, w),
                    "cls": lb[:, 0:1],  # n, 1
                    "bboxes": lb[:, 1:],  # n, 4
                    "normalized": True,
                    "bbox_format": "xywh",
                    "texts": texts,
                }
            )
        return labels

    def build_transforms(self, hyp=None):
        """Configures augmentations for training with optional text loading; `hyp` adjusts augmentation intensity."""
        transforms = super().build_transforms(hyp)
        if self.augment:
            # NOTE: hard-coded the args for now.
            transforms.insert(-1, RandomLoadText(max_samples=80, padding=True))
        return transforms

__init__(*args, task='detect', json_file, **kwargs)

Inizializza un GroundingDataset per il rilevamento degli oggetti, caricando le annotazioni da un file JSON specificato.

Codice sorgente in ultralytics/data/dataset.py
def __init__(self, *args, task="detect", json_file, **kwargs):
    """Initializes a GroundingDataset for object detection, loading annotations from a specified JSON file."""
    assert task == "detect", "`GroundingDataset` only support `detect` task for now!"
    self.json_file = json_file
    super().__init__(*args, task=task, data={}, **kwargs)

build_transforms(hyp=None)

Configura gli aumenti per l'allenamento con il caricamento di testo opzionale; hyp regola l'intensità dell'aumento.

Codice sorgente in ultralytics/data/dataset.py
def build_transforms(self, hyp=None):
    """Configures augmentations for training with optional text loading; `hyp` adjusts augmentation intensity."""
    transforms = super().build_transforms(hyp)
    if self.augment:
        # NOTE: hard-coded the args for now.
        transforms.insert(-1, RandomLoadText(max_samples=80, padding=True))
    return transforms

get_img_files(img_path)

I file immagine verrebbero letti in get_labels restituisce un elenco vuoto.

Codice sorgente in ultralytics/data/dataset.py
def get_img_files(self, img_path):
    """The image files would be read in `get_labels` function, return empty list here."""
    return []

get_labels()

Carica le annotazioni da un file JSON, filtra e normalizza i rettangoli di selezione per ogni immagine.

Codice sorgente in ultralytics/data/dataset.py
def get_labels(self):
    """Loads annotations from a JSON file, filters, and normalizes bounding boxes for each image."""
    labels = []
    LOGGER.info("Loading annotation file...")
    with open(self.json_file, "r") as f:
        annotations = json.load(f)
    images = {f'{x["id"]:d}': x for x in annotations["images"]}
    imgToAnns = defaultdict(list)
    for ann in annotations["annotations"]:
        imgToAnns[ann["image_id"]].append(ann)
    for img_id, anns in TQDM(imgToAnns.items(), desc=f"Reading annotations {self.json_file}"):
        img = images[f"{img_id:d}"]
        h, w, f = img["height"], img["width"], img["file_name"]
        im_file = Path(self.img_path) / f
        if not im_file.exists():
            continue
        self.im_files.append(str(im_file))
        bboxes = []
        cat2id = {}
        texts = []
        for ann in anns:
            if ann["iscrowd"]:
                continue
            box = np.array(ann["bbox"], dtype=np.float32)
            box[:2] += box[2:] / 2
            box[[0, 2]] /= float(w)
            box[[1, 3]] /= float(h)
            if box[2] <= 0 or box[3] <= 0:
                continue

            cat_name = " ".join([img["caption"][t[0] : t[1]] for t in ann["tokens_positive"]])
            if cat_name not in cat2id:
                cat2id[cat_name] = len(cat2id)
                texts.append([cat_name])
            cls = cat2id[cat_name]  # class
            box = [cls] + box.tolist()
            if box not in bboxes:
                bboxes.append(box)
        lb = np.array(bboxes, dtype=np.float32) if len(bboxes) else np.zeros((0, 5), dtype=np.float32)
        labels.append(
            {
                "im_file": im_file,
                "shape": (h, w),
                "cls": lb[:, 0:1],  # n, 1
                "bboxes": lb[:, 1:],  # n, 4
                "normalized": True,
                "bbox_format": "xywh",
                "texts": texts,
            }
        )
    return labels



ultralytics.data.dataset.YOLOConcatDataset

Basi: ConcatDataset

Dataset come concatenazione di più dataset.

Questa classe è utile per assemblare diversi set di dati esistenti.

Codice sorgente in ultralytics/data/dataset.py
class YOLOConcatDataset(ConcatDataset):
    """
    Dataset as a concatenation of multiple datasets.

    This class is useful to assemble different existing datasets.
    """

    @staticmethod
    def collate_fn(batch):
        """Collates data samples into batches."""
        return YOLODataset.collate_fn(batch)

collate_fn(batch) staticmethod

Raccoglie i campioni di dati in lotti.

Codice sorgente in ultralytics/data/dataset.py
@staticmethod
def collate_fn(batch):
    """Collates data samples into batches."""
    return YOLODataset.collate_fn(batch)



ultralytics.data.dataset.SemanticDataset

Basi: BaseDataset

Dataset di segmentazione semantica.

Questa classe è responsabile della gestione dei set di dati utilizzati per le attività di segmentazione semantica. Eredita le funzionalità dalla classe BaseDataset.

Nota

Questa classe è attualmente un segnaposto e deve essere popolata con metodi e attributi per supportare le attività di compiti di segmentazione semantica.

Codice sorgente in 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__()

Inizializza un oggetto SemanticDataset.

Codice sorgente in ultralytics/data/dataset.py
def __init__(self):
    """Initialize a SemanticDataset object."""
    super().__init__()



ultralytics.data.dataset.ClassificationDataset

Estende torchvision ImageFolder per supportare le attività di classificazione di YOLO , offrendo funzionalità come l'aumento delle immagini, il caching e la verifica. aumento delle immagini, caching e verifica. È progettato per gestire in modo efficiente grandi insiemi di dati per l'addestramento di modelli di deep learning. modelli di apprendimento profondo, con trasformazioni opzionali delle immagini e meccanismi di caching per accelerare l'addestramento.

Questa classe consente di aumentare le immagini utilizzando sia le librerie torchvision che Albumentations e supporta la memorizzazione nella cache delle immagini in RAM o su disco per ridurre l'overhead IO durante l'addestramento. Inoltre, implementa un robusto processo di verifica per garantire l'integrità e la coerenza dei dati.

Attributi:

Nome Tipo Descrizione
cache_ram bool

Indica se la cache nella RAM è abilitata.

cache_disk bool

Indica se la cache su disco è abilitata.

samples list

Un elenco di tuple, ognuna delle quali contiene il percorso di un'immagine, il suo indice di classe, il percorso del suo file .npy cache (se la cache è su disco) e, facoltativamente, l'array di immagini caricate (se la cache è in RAM).

torch_transforms callable

PyTorch le trasformazioni da applicare alle immagini.

Codice sorgente in ultralytics/data/dataset.py
class ClassificationDataset:
    """
    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.
        """
        import torchvision  # scope for faster 'import ultralytics'

        # Base class assigned as attribute rather than used as base class to allow for scoping slow torchvision import
        self.base = torchvision.datasets.ImageFolder(root=root)
        self.samples = self.base.samples
        self.root = self.base.root

        # Initialize attributes
        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 str(args.cache).lower() == "ram"  # cache images into RAM
        self.cache_disk = str(args.cache).lower() == "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:
            if im is None:  # Warning: two separate if statements required here, do not combine this with previous line
                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, DATASET_CACHE_VERSION)
        return samples

__getitem__(i)

Restituisce un sottoinsieme di dati e obiettivi corrispondenti agli indici indicati.

Codice sorgente in 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:
        if im is None:  # Warning: two separate if statements required here, do not combine this with previous line
            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='')

Inizializza l'oggetto YOLO con la radice, le dimensioni dell'immagine, gli incrementi e le impostazioni della cache.

Parametri:

Nome Tipo Descrizione Predefinito
root str

Percorso della directory del set di dati in cui sono archiviate le immagini in una struttura di cartelle specifica della classe.

richiesto
args Namespace

Configurazione contenente le impostazioni relative al set di dati, come le dimensioni dell'immagine, i parametri di incremento e le impostazioni della cache. e le impostazioni della cache. Include attributi come imgsz (dimensione dell'immagine), fraction (frazione dei dati da utilizzare), scale, fliplr, flipud, cache (cache su disco o RAM per una formazione più veloce), auto_augment, hsv_h, hsv_s, hsv_v, e crop_fraction.

richiesto
augment bool

Se applicare o meno gli aumenti al set di dati. L'impostazione predefinita è False.

False
prefix str

Prefisso per i nomi dei file di registrazione e della cache, che aiuta a identificare i set di dati e a eseguire il debug. debug. Il valore predefinito è una stringa vuota.

''
Codice sorgente in 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.
    """
    import torchvision  # scope for faster 'import ultralytics'

    # Base class assigned as attribute rather than used as base class to allow for scoping slow torchvision import
    self.base = torchvision.datasets.ImageFolder(root=root)
    self.samples = self.base.samples
    self.root = self.base.root

    # Initialize attributes
    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 str(args.cache).lower() == "ram"  # cache images into RAM
    self.cache_disk = str(args.cache).lower() == "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__()

Restituisce il numero totale di campioni nel set di dati.

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

verify_images()

Verifica tutte le immagini del set di dati.

Codice sorgente in 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, DATASET_CACHE_VERSION)
    return samples





Created 2023-11-12, Updated 2024-06-02
Authors: glenn-jocher (6), Burhan-Q (1), Laughing-q (1)