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Referência para ultralytics/data/dataset.py

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ultralytics.data.dataset.YOLODataset

Bases: BaseDataset

Classe de conjunto de dados para carregar etiquetas de deteção e/ou segmentação de objectos no formato YOLO .

Parâmetros:

Nome Tipo Descrição Predefinição
data dict

Um dicionário YAML de conjunto de dados. A predefinição é Nenhum.

None
task str

Um arg explícito para apontar a tarefa atual. A predefinição é "detetar".

'detect'

Devolve:

Tipo Descrição
Dataset

Um objeto do conjunto de dados PyTorch que pode ser utilizado para treinar um modelo de deteção de objectos.

Código fonte em 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)

Inicializa o YOLODataset com configurações opcionais para segmentos e pontos-chave.

Código fonte em 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)

Constrói e acrescenta transformações à lista.

Código fonte em 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'))

Armazena em cache as etiquetas do conjunto de dados, verifica as imagens e lê as formas.

Parâmetros:

Nome Tipo Descrição Predefinição
path Path

Caminho onde guardar o ficheiro de cache. A predefinição é Path('./labels.cache').

Path('./labels.cache')

Devolve:

Tipo Descrição
dict

etiquetas.

Código fonte em 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)

Define as opções mosaic, copy_paste e mixup para 0.0 e constrói as transformações.

Código fonte em 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

Agrupa as amostras de dados em lotes.

Código fonte em 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()

Devolve o dicionário de etiquetas para o treino YOLO .

Código fonte em 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)

Personaliza o formato da tua etiqueta aqui.

Nota

A classificação e a segmentação semântica precisam de uma etiqueta cls independente. Também podes apoiar a classificação e a segmentação semântica adicionando ou removendo chaves dict.

Código fonte em 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

Bases: YOLODataset

Classe de conjunto de dados para carregar etiquetas de deteção e/ou segmentação de objectos no formato YOLO .

Parâmetros:

Nome Tipo Descrição Predefinição
data dict

Um dicionário YAML de conjunto de dados. A predefinição é Nenhum.

None
task str

Um arg explícito para apontar a tarefa atual. A predefinição é "detetar".

'detect'

Devolve:

Tipo Descrição
Dataset

Um objeto do conjunto de dados PyTorch que pode ser utilizado para treinar um modelo de deteção de objectos.

Código fonte em 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)

Inicializa um objeto de conjunto de dados para tarefas de deteção de objectos com especificações opcionais.

Código fonte em 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)

Melhora as transformações de dados com aumento de texto opcional para formação multimodal.

Código fonte em 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)

Adiciona informações de textos para o treino de modelos multimodais.

Código fonte em 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

Bases: YOLODataset

Código fonte em 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)

Inicializa um GroundingDataset para deteção de objectos, carregando anotações de um ficheiro JSON especificado.

Código fonte em 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 as ampliações para treino com carregamento de texto opcional; hyp ajusta a intensidade do aumento.

Código fonte em 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)

Os ficheiros de imagem seriam lidos em get_labels devolve aqui a lista vazia.

Código fonte em 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()

Carrega anotações de um arquivo JSON, filtra e normaliza as caixas delimitadoras de cada imagem.

Código fonte em 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

Bases: ConcatDataset

Conjunto de dados como uma concatenação de vários conjuntos de dados.

Esta classe é útil para reunir diferentes conjuntos de dados existentes.

Código fonte em 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

Agrupa as amostras de dados em lotes.

Código fonte em ultralytics/data/dataset.py
@staticmethod
def collate_fn(batch):
    """Collates data samples into batches."""
    return YOLODataset.collate_fn(batch)



ultralytics.data.dataset.SemanticDataset

Bases: BaseDataset

Conjunto de dados de segmentação semântica.

Esta classe é responsável pelo tratamento de conjuntos de dados utilizados em tarefas de segmentação semântica. Herda as funcionalidades da classe BaseDataset.

Nota

Esta classe é atualmente um espaço reservado e precisa de ser preenchida com métodos e atributos para apoiar tarefas de segmentação semântica.

Código fonte em 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__()

Inicializa um objeto SemanticDataset.

Código fonte em ultralytics/data/dataset.py
def __init__(self):
    """Initialize a SemanticDataset object."""
    super().__init__()



ultralytics.data.dataset.ClassificationDataset

Estende o torchvision ImageFolder para suportar YOLO tarefas de classificação, oferecendo funcionalidades como o aumento de imagens, o armazenamento em cache e a verificação. como aumento de imagem, armazenamento em cache e verificação. Foi concebido para lidar eficientemente com grandes conjuntos de dados para treinar modelos de aprendizagem modelos de aprendizagem profunda, com transformações de imagem opcionais e mecanismos de cache para acelerar o treinamento.

Esta classe permite aumentos usando as bibliotecas torchvision e Albumentations, e suporta imagens em cache na RAM ou no disco para reduzir a sobrecarga de IO durante o treino. Além disso, implementa um processo de verificação robusto para garantir a integridade e consistência dos dados.

Atributos:

Nome Tipo Descrição
cache_ram bool

Indica se o armazenamento em cache na RAM está ativado.

cache_disk bool

Indica se o armazenamento em cache no disco está ativado.

samples list

Uma lista de tuplas, cada uma contendo o caminho para uma imagem, seu índice de classe, caminho para seu arquivo de cache .npy (se estiver em cache no disco) e, opcionalmente, a matriz de imagem carregada (se estiver em cache na RAM).

torch_transforms callable

PyTorch transforma a ser aplicada às imagens.

Código fonte em 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)

Devolve o subconjunto de dados e alvos correspondentes aos índices indicados.

Código fonte em 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='')

Inicializa o objeto YOLO com a raiz, o tamanho da imagem, os aumentos e as definições de cache.

Parâmetros:

Nome Tipo Descrição Predefinição
root str

Caminho para o diretório do conjunto de dados onde as imagens são armazenadas numa estrutura de pastas específica da classe.

necessário
args Namespace

Configuração que contém definições relacionadas com o conjunto de dados, como o tamanho da imagem, parâmetros de aumento e definições de cache. Inclui atributos como imgsz (tamanho da imagem), fraction (fração dos dados a utilizar), scale, fliplr, flipud, cache (armazenamento em disco ou na RAM para um treino mais rápido), auto_augment, hsv_h, hsv_s, hsv_ve crop_fraction.

necessário
augment bool

Aplica ou não aumentos ao conjunto de dados. A predefinição é Falso.

False
prefix str

Prefixo para nomes de ficheiros de registo e de cache, ajudando na identificação do conjunto de dados e depuração. A predefinição é uma cadeia vazia.

''
Código fonte em 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__()

Devolve o número total de amostras no conjunto de dados.

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

verify_images()

Verifica todas as imagens no conjunto de dados.

Código fonte em 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)