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custom_datasets

sleap_nn.data.custom_datasets

Custom torch.utils.data.Datasets for different model types.

Classes:

Name Description
BaseDataset

Base class for custom torch Datasets.
BottomUpDataset

Dataset class for bottom-up models.
BottomUpMultiClassDataset

Dataset class for bottom-up ID models.
CenteredInstanceDataset

Dataset class for instance-centered confidence map models.
CentroidDataset

Dataset class for centroid models.
InfiniteDataLoader

Dataloader that reuses workers for infinite iteration.
SingleInstanceDataset

Dataset class for single-instance models.
TopDownCenteredInstanceMultiClassDataset

Dataset class for instance-centered confidence map ID models.

Functions:

Name Description
get_steps_per_epoch

Compute the number of steps (iterations) per epoch for the given dataset.
get_train_val_dataloaders

Return the train and val dataloaders.
get_train_val_datasets

Return the train and val datasets.

BaseDataset

Bases: Dataset

Base class for custom torch Datasets.

Attributes:

Name Type Description
labels

Source sio.Labels object.
max_stride

Scalar integer specifying the maximum stride that the image must be divisible by.
user_instances_only

True if only user labeled instances should be used for training. If False, both user labeled and predicted instances would be used.
ensure_rgb

(bool) True if the input image should have 3 channels (RGB image). If input has only one
is replicated along the channel axis. If the image has three channels and this is set to False, then we retain the three channels. Default

False.
ensure_grayscale

(bool) True if the input image should only have a single channel. If input has three channels (RGB) and this
image. If the source image has only one channel and this is set to False, then we retain the single channel input. Default

False.
intensity_aug

Intensity augmentation configuration. Can be: - String: One of ['uniform_noise', 'gaussian_noise', 'contrast', 'brightness'] - List of strings: Multiple intensity augmentations from the allowed values - Dictionary: Custom intensity configuration - None: No intensity augmentation applied
geometric_aug

Geometric augmentation configuration. Can be: - String: One of ['rotation', 'scale', 'translate', 'erase_scale', 'mixup'] - List of strings: Multiple geometric augmentations from the allowed values - Dictionary: Custom geometric configuration - None: No geometric augmentation applied
scale

Factor to resize the image dimensions by, specified as a float. Default: 1.0.
apply_aug

True if augmentations should be applied to the data pipeline, else False. Default: False.
max_hw

Maximum height and width of images across the labels file. If max_height and max_width in the config is None, then max_hw is used (computed with sleap_nn.data.providers.get_max_height_width). Else the values in the config are used.
cache_img

String to indicate which caching to use: memory or disk. If None, the images aren't cached and loaded from the .slp file on each access.
cache_img_path

Path to save the .jpg files. If None, current working dir is used.
use_existing_imgs

Use existing imgs/ chunks in the cache_img_path.
rank

Indicates the rank of the process. Used during distributed training to ensure that image storage to disk occurs only once across all workers.

Methods:

Name Description
__getitem__

Returns the sample dict for given index.
__init__

Initialize class attributes.
__iter__

Returns an iterator.
__len__

Return the number of samples in the dataset.
__next__

Get the next sample from the dataset.
Source code in sleap_nn/data/custom_datasets.py 
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class BaseDataset(Dataset):
    """Base class for custom torch Datasets.

    Attributes:
        labels: Source `sio.Labels` object.
        max_stride: Scalar integer specifying the maximum stride that the image must be
            divisible by.
        user_instances_only: `True` if only user labeled instances should be used for training. If `False`,
            both user labeled and predicted instances would be used.
        ensure_rgb: (bool) True if the input image should have 3 channels (RGB image). If input has only one
        channel when this is set to `True`, then the images from single-channel
        is replicated along the channel axis. If the image has three channels and this is set to False, then we retain the three channels. Default: `False`.
        ensure_grayscale: (bool) True if the input image should only have a single channel. If input has three channels (RGB) and this
        is set to True, then we convert the image to grayscale (single-channel)
        image. If the source image has only one channel and this is set to False, then we retain the single channel input. Default: `False`.
        intensity_aug: Intensity augmentation configuration. Can be:
            - String: One of ['uniform_noise', 'gaussian_noise', 'contrast', 'brightness']
            - List of strings: Multiple intensity augmentations from the allowed values
            - Dictionary: Custom intensity configuration
            - None: No intensity augmentation applied
        geometric_aug: Geometric augmentation configuration. Can be:
            - String: One of ['rotation', 'scale', 'translate', 'erase_scale', 'mixup']
            - List of strings: Multiple geometric augmentations from the allowed values
            - Dictionary: Custom geometric configuration
            - None: No geometric augmentation applied
        scale: Factor to resize the image dimensions by, specified as a float. Default: 1.0.
        apply_aug: `True` if augmentations should be applied to the data pipeline,
            else `False`. Default: `False`.
        max_hw: Maximum height and width of images across the labels file. If `max_height` and
           `max_width` in the config is None, then `max_hw` is used (computed with
            `sleap_nn.data.providers.get_max_height_width`). Else the values in the config
            are used.
        cache_img: String to indicate which caching to use: `memory` or `disk`. If `None`,
            the images aren't cached and loaded from the `.slp` file on each access.
        cache_img_path: Path to save the `.jpg` files. If `None`, current working dir is used.
        use_existing_imgs: Use existing imgs/ chunks in the `cache_img_path`.
        rank: Indicates the rank of the process. Used during distributed training to ensure that image storage to
            disk occurs only once across all workers.
    """

    def __init__(
        self,
        labels: List[sio.Labels],
        max_stride: int,
        user_instances_only: bool = True,
        ensure_rgb: bool = False,
        ensure_grayscale: bool = False,
        intensity_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
        geometric_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
        scale: float = 1.0,
        apply_aug: bool = False,
        max_hw: Tuple[Optional[int]] = (None, None),
        cache_img: Optional[str] = None,
        cache_img_path: Optional[str] = None,
        use_existing_imgs: bool = False,
        rank: Optional[int] = None,
    ) -> None:
        """Initialize class attributes."""
        super().__init__()
        self.labels = labels
        self.user_instances_only = user_instances_only
        self.ensure_rgb = ensure_rgb
        self.ensure_grayscale = ensure_grayscale

        # Handle intensity augmentation
        if intensity_aug is not None:
            if not isinstance(intensity_aug, DictConfig):
                intensity_aug = get_aug_config(intensity_aug=intensity_aug)
                config = OmegaConf.structured(intensity_aug)
                OmegaConf.to_container(config, resolve=True, throw_on_missing=True)
                intensity_aug = DictConfig(config.intensity)
        self.intensity_aug = intensity_aug

        # Handle geometric augmentation
        if geometric_aug is not None:
            if not isinstance(geometric_aug, DictConfig):
                geometric_aug = get_aug_config(geometric_aug=geometric_aug)
                config = OmegaConf.structured(geometric_aug)
                OmegaConf.to_container(config, resolve=True, throw_on_missing=True)
                geometric_aug = DictConfig(config.geometric)
        self.geometric_aug = geometric_aug
        self.curr_idx = 0
        self.max_stride = max_stride
        self.scale = scale
        self.apply_aug = apply_aug
        self.max_hw = max_hw
        self.rank = rank
        self.max_instances = 0
        for x in self.labels:
            max_instances = get_max_instances(x) if x else None

            if max_instances > self.max_instances:
                self.max_instances = max_instances

        self.lf_idx_list = self._get_lf_idx_list()
        self.cache_img = cache_img
        self.cache_img_path = cache_img_path
        self.use_existing_imgs = use_existing_imgs
        if self.cache_img is not None and "disk" in self.cache_img:
            if self.cache_img_path is None:
                self.cache_img_path = "."
            path = (
                Path(self.cache_img_path)
                if isinstance(self.cache_img_path, str)
                else self.cache_img_path
            )
            if not path.is_dir():
                path.mkdir(parents=True, exist_ok=True)

        self.transform_to_pil = T.ToPILImage()
        self.transform_pil_to_tensor = T.ToTensor()
        self.cache = {}

        if self.cache_img is not None:
            if self.cache_img == "memory":
                self._fill_cache()
            elif self.cache_img == "disk" and not self.use_existing_imgs:
                if self.rank is None or self.rank == -1 or self.rank == 0:
                    self._fill_cache()
                # Synchronize all ranks after cache creation
                if is_distributed_initialized():
                    dist.barrier()

    def _get_lf_idx_list(self) -> List[Tuple[int]]:
        """Return list of indices of labelled frames."""
        lf_idx_list = []
        for labels_idx, label in enumerate(self.labels):
            for lf_idx, lf in enumerate(label):
                # Filter to user instances
                if self.user_instances_only:
                    if lf.user_instances is not None and len(lf.user_instances) > 0:
                        lf.instances = lf.user_instances
                is_empty = True
                for _, inst in enumerate(lf.instances):
                    if not inst.is_empty:  # filter all NaN instances.
                        is_empty = False
                if not is_empty:
                    lf_idx_list.append((labels_idx, lf_idx))
        return lf_idx_list

    def __next__(self):
        """Get the next sample from the dataset."""
        if self.curr_idx >= len(self):
            raise StopIteration

        sample = self.__getitem__(self.curr_idx)
        self.curr_idx += 1
        return sample

    def __iter__(self):
        """Returns an iterator."""
        return self

    def _fill_cache(self):
        """Load all samples to cache."""
        # TODO: Implement parallel processing (using threads might cause error with MediaVideo backend)
        for labels_idx, lf_idx in self.lf_idx_list:
            img = self.labels[labels_idx][lf_idx].image
            if img.shape[-1] == 1:
                img = np.squeeze(img)
            if self.cache_img == "disk":
                f_name = f"{self.cache_img_path}/sample_{labels_idx}_{lf_idx}.jpg"
                Image.fromarray(img).save(f_name, format="JPEG")
            if self.cache_img == "memory":
                self.cache[(labels_idx, lf_idx)] = img

    def _get_video_idx(self, lf, labels_idx):
        """Return indsample of `lf.video` in `labels.videos`."""
        return self.labels[labels_idx].videos.index(lf.video)

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

    def __getitem__(self, index) -> Dict:
        """Returns the sample dict for given index."""
        message = "Subclasses must implement __getitem__"
        logger.error(message)
        raise NotImplementedError(message)

__getitem__(index)

Returns the sample dict for given index.

Source code in sleap_nn/data/custom_datasets.py 
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def __getitem__(self, index) -> Dict:
    """Returns the sample dict for given index."""
    message = "Subclasses must implement __getitem__"
    logger.error(message)
    raise NotImplementedError(message)

__init__(labels, max_stride, user_instances_only=True, ensure_rgb=False, ensure_grayscale=False, intensity_aug=None, geometric_aug=None, scale=1.0, apply_aug=False, max_hw=(None, None), cache_img=None, cache_img_path=None, use_existing_imgs=False, rank=None)

Initialize class attributes.

Source code in sleap_nn/data/custom_datasets.py 
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def __init__(
    self,
    labels: List[sio.Labels],
    max_stride: int,
    user_instances_only: bool = True,
    ensure_rgb: bool = False,
    ensure_grayscale: bool = False,
    intensity_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
    geometric_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
    scale: float = 1.0,
    apply_aug: bool = False,
    max_hw: Tuple[Optional[int]] = (None, None),
    cache_img: Optional[str] = None,
    cache_img_path: Optional[str] = None,
    use_existing_imgs: bool = False,
    rank: Optional[int] = None,
) -> None:
    """Initialize class attributes."""
    super().__init__()
    self.labels = labels
    self.user_instances_only = user_instances_only
    self.ensure_rgb = ensure_rgb
    self.ensure_grayscale = ensure_grayscale

    # Handle intensity augmentation
    if intensity_aug is not None:
        if not isinstance(intensity_aug, DictConfig):
            intensity_aug = get_aug_config(intensity_aug=intensity_aug)
            config = OmegaConf.structured(intensity_aug)
            OmegaConf.to_container(config, resolve=True, throw_on_missing=True)
            intensity_aug = DictConfig(config.intensity)
    self.intensity_aug = intensity_aug

    # Handle geometric augmentation
    if geometric_aug is not None:
        if not isinstance(geometric_aug, DictConfig):
            geometric_aug = get_aug_config(geometric_aug=geometric_aug)
            config = OmegaConf.structured(geometric_aug)
            OmegaConf.to_container(config, resolve=True, throw_on_missing=True)
            geometric_aug = DictConfig(config.geometric)
    self.geometric_aug = geometric_aug
    self.curr_idx = 0
    self.max_stride = max_stride
    self.scale = scale
    self.apply_aug = apply_aug
    self.max_hw = max_hw
    self.rank = rank
    self.max_instances = 0
    for x in self.labels:
        max_instances = get_max_instances(x) if x else None

        if max_instances > self.max_instances:
            self.max_instances = max_instances

    self.lf_idx_list = self._get_lf_idx_list()
    self.cache_img = cache_img
    self.cache_img_path = cache_img_path
    self.use_existing_imgs = use_existing_imgs
    if self.cache_img is not None and "disk" in self.cache_img:
        if self.cache_img_path is None:
            self.cache_img_path = "."
        path = (
            Path(self.cache_img_path)
            if isinstance(self.cache_img_path, str)
            else self.cache_img_path
        )
        if not path.is_dir():
            path.mkdir(parents=True, exist_ok=True)

    self.transform_to_pil = T.ToPILImage()
    self.transform_pil_to_tensor = T.ToTensor()
    self.cache = {}

    if self.cache_img is not None:
        if self.cache_img == "memory":
            self._fill_cache()
        elif self.cache_img == "disk" and not self.use_existing_imgs:
            if self.rank is None or self.rank == -1 or self.rank == 0:
                self._fill_cache()
            # Synchronize all ranks after cache creation
            if is_distributed_initialized():
                dist.barrier()

__iter__()

Returns an iterator.

Source code in sleap_nn/data/custom_datasets.py 
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def __iter__(self):
    """Returns an iterator."""
    return self

__len__()

Return the number of samples in the dataset.

Source code in sleap_nn/data/custom_datasets.py 
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def __len__(self) -> int:
    """Return the number of samples in the dataset."""
    return len(self.lf_idx_list)

__next__()

Get the next sample from the dataset.

Source code in sleap_nn/data/custom_datasets.py 
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def __next__(self):
    """Get the next sample from the dataset."""
    if self.curr_idx >= len(self):
        raise StopIteration

    sample = self.__getitem__(self.curr_idx)
    self.curr_idx += 1
    return sample

BottomUpDataset

Bases: BaseDataset

Dataset class for bottom-up models.

Attributes:

Name Type Description
labels

Source sio.Labels object.
max_stride

Scalar integer specifying the maximum stride that the image must be divisible by.
user_instances_only

True if only user labeled instances should be used for training. If False, both user labeled and predicted instances would be used.
ensure_rgb

(bool) True if the input image should have 3 channels (RGB image). If input has only one
is replicated along the channel axis. If the image has three channels and this is set to False, then we retain the three channels. Default

False.
ensure_grayscale

(bool) True if the input image should only have a single channel. If input has three channels (RGB) and this
image. If the source image has only one channel and this is set to False, then we retain the single channel input. Default

False.
intensity_aug

Intensity augmentation configuration. Can be: - String: One of ['uniform_noise', 'gaussian_noise', 'contrast', 'brightness'] - List of strings: Multiple intensity augmentations from the allowed values - Dictionary: Custom intensity configuration - None: No intensity augmentation applied
geometric_aug

Geometric augmentation configuration. Can be: - String: One of ['rotation', 'scale', 'translate', 'erase_scale', 'mixup'] - List of strings: Multiple geometric augmentations from the allowed values - Dictionary: Custom geometric configuration - None: No geometric augmentation applied
scale

Factor to resize the image dimensions by, specified as a float. Default: 1.0.
apply_aug

True if augmentations should be applied to the data pipeline, else False. Default: False.
max_hw

Maximum height and width of images across the labels file. If max_height and max_width in the config is None, then max_hw is used (computed with sleap_nn.data.providers.get_max_height_width). Else the values in the config are used.
confmap_head_config

DictConfig object with all the keys in the head_config section. (required keys: sigma, output_stride and anchor_part depending on the model type ).
pafs_head_config

DictConfig object with all the keys in the head_config section (required keys: sigma, output_stride and anchor_part depending on the model type ) for PAFs.
cache_img

String to indicate which caching to use: memory or disk. If None, the images aren't cached and loaded from the .slp file on each access.
cache_img_path

Path to save the .jpg files. If None, current working dir is used.
use_existing_imgs

Use existing imgs/ chunks in the cache_img_path.
rank

Indicates the rank of the process. Used during distributed training to ensure that image storage to disk occurs only once across all workers.

Methods:

Name Description
__getitem__

Return dict with image, confmaps and pafs for given index.
__init__

Initialize class attributes.
Source code in sleap_nn/data/custom_datasets.py 
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class BottomUpDataset(BaseDataset):
    """Dataset class for bottom-up models.

    Attributes:
        labels: Source `sio.Labels` object.
        max_stride: Scalar integer specifying the maximum stride that the image must be
            divisible by.
        user_instances_only: `True` if only user labeled instances should be used for training. If `False`,
            both user labeled and predicted instances would be used.
        ensure_rgb: (bool) True if the input image should have 3 channels (RGB image). If input has only one
        channel when this is set to `True`, then the images from single-channel
        is replicated along the channel axis. If the image has three channels and this is set to False, then we retain the three channels. Default: `False`.
        ensure_grayscale: (bool) True if the input image should only have a single channel. If input has three channels (RGB) and this
        is set to True, then we convert the image to grayscale (single-channel)
        image. If the source image has only one channel and this is set to False, then we retain the single channel input. Default: `False`.
        intensity_aug: Intensity augmentation configuration. Can be:
            - String: One of ['uniform_noise', 'gaussian_noise', 'contrast', 'brightness']
            - List of strings: Multiple intensity augmentations from the allowed values
            - Dictionary: Custom intensity configuration
            - None: No intensity augmentation applied
        geometric_aug: Geometric augmentation configuration. Can be:
            - String: One of ['rotation', 'scale', 'translate', 'erase_scale', 'mixup']
            - List of strings: Multiple geometric augmentations from the allowed values
            - Dictionary: Custom geometric configuration
            - None: No geometric augmentation applied
        scale: Factor to resize the image dimensions by, specified as a float. Default: 1.0.
        apply_aug: `True` if augmentations should be applied to the data pipeline,
            else `False`. Default: `False`.
        max_hw: Maximum height and width of images across the labels file. If `max_height` and
           `max_width` in the config is None, then `max_hw` is used (computed with
            `sleap_nn.data.providers.get_max_height_width`). Else the values in the config
            are used.
        confmap_head_config: DictConfig object with all the keys in the `head_config` section.
            (required keys: `sigma`, `output_stride` and `anchor_part` depending on the model type ).
        pafs_head_config: DictConfig object with all the keys in the `head_config` section
            (required keys: `sigma`, `output_stride` and `anchor_part` depending on the model type )
            for PAFs.
        cache_img: String to indicate which caching to use: `memory` or `disk`. If `None`,
            the images aren't cached and loaded from the `.slp` file on each access.
        cache_img_path: Path to save the `.jpg` files. If `None`, current working dir is used.
        use_existing_imgs: Use existing imgs/ chunks in the `cache_img_path`.
        rank: Indicates the rank of the process. Used during distributed training to ensure that image storage to
            disk occurs only once across all workers.
    """

    def __init__(
        self,
        labels: List[sio.Labels],
        confmap_head_config: DictConfig,
        pafs_head_config: DictConfig,
        max_stride: int,
        user_instances_only: bool = True,
        ensure_rgb: bool = False,
        ensure_grayscale: bool = False,
        intensity_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
        geometric_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
        scale: float = 1.0,
        apply_aug: bool = False,
        max_hw: Tuple[Optional[int]] = (None, None),
        cache_img: Optional[str] = None,
        cache_img_path: Optional[str] = None,
        use_existing_imgs: bool = False,
        rank: Optional[int] = None,
    ) -> None:
        """Initialize class attributes."""
        super().__init__(
            labels=labels,
            max_stride=max_stride,
            user_instances_only=user_instances_only,
            ensure_rgb=ensure_rgb,
            ensure_grayscale=ensure_grayscale,
            intensity_aug=intensity_aug,
            geometric_aug=geometric_aug,
            scale=scale,
            apply_aug=apply_aug,
            max_hw=max_hw,
            cache_img=cache_img,
            cache_img_path=cache_img_path,
            use_existing_imgs=use_existing_imgs,
            rank=rank,
        )
        self.confmap_head_config = confmap_head_config
        self.pafs_head_config = pafs_head_config

        self.edge_inds = self.labels[0].skeletons[0].edge_inds

    def __getitem__(self, index) -> Dict:
        """Return dict with image, confmaps and pafs for given index."""
        (labels_idx, lf_idx) = self.lf_idx_list[index]

        lf = self.labels[labels_idx][lf_idx]

        # load the img
        if self.cache_img is not None:
            if self.cache_img == "disk":
                img = np.array(
                    Image.open(
                        f"{self.cache_img_path}/sample_{labels_idx}_{lf_idx}.jpg"
                    )
                )
            elif self.cache_img == "memory":
                img = self.cache[(labels_idx, lf_idx)].copy()

        else:  # load from slp file if not cached
            img = lf.image

        if img.ndim == 2:
            img = np.expand_dims(img, axis=2)

        video_idx = self._get_video_idx(lf, labels_idx)

        # get dict
        sample = process_lf(
            lf,
            video_idx=video_idx,
            max_instances=self.max_instances,
            user_instances_only=self.user_instances_only,
        )

        # apply normalization
        sample["image"] = apply_normalization(sample["image"])

        if self.ensure_rgb:
            sample["image"] = convert_to_rgb(sample["image"])
        elif self.ensure_grayscale:
            sample["image"] = convert_to_grayscale(sample["image"])

        # size matcher
        sample["image"], eff_scale = apply_sizematcher(
            sample["image"],
            max_height=self.max_hw[0],
            max_width=self.max_hw[1],
        )
        sample["instances"] = sample["instances"] * eff_scale
        sample["eff_scale"] = torch.tensor(eff_scale, dtype=torch.float32)

        # resize image
        sample["image"], sample["instances"] = apply_resizer(
            sample["image"],
            sample["instances"],
            scale=self.scale,
        )

        # Pad the image (if needed) according max stride
        sample["image"] = apply_pad_to_stride(
            sample["image"], max_stride=self.max_stride
        )

        # apply augmentation
        if self.apply_aug:
            if self.intensity_aug is not None:
                sample["image"], sample["instances"] = apply_intensity_augmentation(
                    sample["image"],
                    sample["instances"],
                    **self.intensity_aug,
                )

            if self.geometric_aug is not None:
                sample["image"], sample["instances"] = apply_geometric_augmentation(
                    sample["image"],
                    sample["instances"],
                    **self.geometric_aug,
                )

        img_hw = sample["image"].shape[-2:]

        # Generate confidence maps
        confidence_maps = generate_multiconfmaps(
            sample["instances"],
            img_hw=img_hw,
            num_instances=sample["num_instances"],
            sigma=self.confmap_head_config.sigma,
            output_stride=self.confmap_head_config.output_stride,
            is_centroids=False,
        )

        # pafs
        pafs = generate_pafs(
            sample["instances"],
            img_hw=img_hw,
            sigma=self.pafs_head_config.sigma,
            output_stride=self.pafs_head_config.output_stride,
            edge_inds=torch.Tensor(self.edge_inds),
            flatten_channels=True,
        )

        sample["confidence_maps"] = confidence_maps
        sample["part_affinity_fields"] = pafs
        sample["labels_idx"] = labels_idx

        return sample

__getitem__(index)

Return dict with image, confmaps and pafs for given index.

Source code in sleap_nn/data/custom_datasets.py 
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def __getitem__(self, index) -> Dict:
    """Return dict with image, confmaps and pafs for given index."""
    (labels_idx, lf_idx) = self.lf_idx_list[index]

    lf = self.labels[labels_idx][lf_idx]

    # load the img
    if self.cache_img is not None:
        if self.cache_img == "disk":
            img = np.array(
                Image.open(
                    f"{self.cache_img_path}/sample_{labels_idx}_{lf_idx}.jpg"
                )
            )
        elif self.cache_img == "memory":
            img = self.cache[(labels_idx, lf_idx)].copy()

    else:  # load from slp file if not cached
        img = lf.image

    if img.ndim == 2:
        img = np.expand_dims(img, axis=2)

    video_idx = self._get_video_idx(lf, labels_idx)

    # get dict
    sample = process_lf(
        lf,
        video_idx=video_idx,
        max_instances=self.max_instances,
        user_instances_only=self.user_instances_only,
    )

    # apply normalization
    sample["image"] = apply_normalization(sample["image"])

    if self.ensure_rgb:
        sample["image"] = convert_to_rgb(sample["image"])
    elif self.ensure_grayscale:
        sample["image"] = convert_to_grayscale(sample["image"])

    # size matcher
    sample["image"], eff_scale = apply_sizematcher(
        sample["image"],
        max_height=self.max_hw[0],
        max_width=self.max_hw[1],
    )
    sample["instances"] = sample["instances"] * eff_scale
    sample["eff_scale"] = torch.tensor(eff_scale, dtype=torch.float32)

    # resize image
    sample["image"], sample["instances"] = apply_resizer(
        sample["image"],
        sample["instances"],
        scale=self.scale,
    )

    # Pad the image (if needed) according max stride
    sample["image"] = apply_pad_to_stride(
        sample["image"], max_stride=self.max_stride
    )

    # apply augmentation
    if self.apply_aug:
        if self.intensity_aug is not None:
            sample["image"], sample["instances"] = apply_intensity_augmentation(
                sample["image"],
                sample["instances"],
                **self.intensity_aug,
            )

        if self.geometric_aug is not None:
            sample["image"], sample["instances"] = apply_geometric_augmentation(
                sample["image"],
                sample["instances"],
                **self.geometric_aug,
            )

    img_hw = sample["image"].shape[-2:]

    # Generate confidence maps
    confidence_maps = generate_multiconfmaps(
        sample["instances"],
        img_hw=img_hw,
        num_instances=sample["num_instances"],
        sigma=self.confmap_head_config.sigma,
        output_stride=self.confmap_head_config.output_stride,
        is_centroids=False,
    )

    # pafs
    pafs = generate_pafs(
        sample["instances"],
        img_hw=img_hw,
        sigma=self.pafs_head_config.sigma,
        output_stride=self.pafs_head_config.output_stride,
        edge_inds=torch.Tensor(self.edge_inds),
        flatten_channels=True,
    )

    sample["confidence_maps"] = confidence_maps
    sample["part_affinity_fields"] = pafs
    sample["labels_idx"] = labels_idx

    return sample

__init__(labels, confmap_head_config, pafs_head_config, max_stride, user_instances_only=True, ensure_rgb=False, ensure_grayscale=False, intensity_aug=None, geometric_aug=None, scale=1.0, apply_aug=False, max_hw=(None, None), cache_img=None, cache_img_path=None, use_existing_imgs=False, rank=None)

Initialize class attributes.

Source code in sleap_nn/data/custom_datasets.py 
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def __init__(
    self,
    labels: List[sio.Labels],
    confmap_head_config: DictConfig,
    pafs_head_config: DictConfig,
    max_stride: int,
    user_instances_only: bool = True,
    ensure_rgb: bool = False,
    ensure_grayscale: bool = False,
    intensity_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
    geometric_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
    scale: float = 1.0,
    apply_aug: bool = False,
    max_hw: Tuple[Optional[int]] = (None, None),
    cache_img: Optional[str] = None,
    cache_img_path: Optional[str] = None,
    use_existing_imgs: bool = False,
    rank: Optional[int] = None,
) -> None:
    """Initialize class attributes."""
    super().__init__(
        labels=labels,
        max_stride=max_stride,
        user_instances_only=user_instances_only,
        ensure_rgb=ensure_rgb,
        ensure_grayscale=ensure_grayscale,
        intensity_aug=intensity_aug,
        geometric_aug=geometric_aug,
        scale=scale,
        apply_aug=apply_aug,
        max_hw=max_hw,
        cache_img=cache_img,
        cache_img_path=cache_img_path,
        use_existing_imgs=use_existing_imgs,
        rank=rank,
    )
    self.confmap_head_config = confmap_head_config
    self.pafs_head_config = pafs_head_config

    self.edge_inds = self.labels[0].skeletons[0].edge_inds

BottomUpMultiClassDataset

Bases: BaseDataset

Dataset class for bottom-up ID models.

Attributes:

Name Type Description
labels

Source sio.Labels object.
max_stride

Scalar integer specifying the maximum stride that the image must be divisible by.
class_map_threshold

Minimum confidence map value below which map values will be replaced with zeros.
user_instances_only

True if only user labeled instances should be used for training. If False, both user labeled and predicted instances would be used.
ensure_rgb

(bool) True if the input image should have 3 channels (RGB image). If input has only one
is replicated along the channel axis. If the image has three channels and this is set to False, then we retain the three channels. Default

False.
ensure_grayscale

(bool) True if the input image should only have a single channel. If input has three channels (RGB) and this
image. If the source image has only one channel and this is set to False, then we retain the single channel input. Default

False.
intensity_aug

Intensity augmentation configuration. Can be: - String: One of ['uniform_noise', 'gaussian_noise', 'contrast', 'brightness'] - List of strings: Multiple intensity augmentations from the allowed values - Dictionary: Custom intensity configuration - None: No intensity augmentation applied
geometric_aug

Geometric augmentation configuration. Can be: - String: One of ['rotation', 'scale', 'translate', 'erase_scale', 'mixup'] - List of strings: Multiple geometric augmentations from the allowed values - Dictionary: Custom geometric configuration - None: No geometric augmentation applied
scale

Factor to resize the image dimensions by, specified as a float. Default: 1.0.
apply_aug

True if augmentations should be applied to the data pipeline, else False. Default: False.
max_hw

Maximum height and width of images across the labels file. If max_height and max_width in the config is None, then max_hw is used (computed with sleap_nn.data.providers.get_max_height_width). Else the values in the config are used.
confmap_head_config

DictConfig object with all the keys in the head_config section. (required keys: sigma, output_stride and anchor_part depending on the model type ).
class_maps_head_config

DictConfig object with all the keys in the head_config section (required keys: sigma, output_stride and classes) for class maps.
cache_img

String to indicate which caching to use: memory or disk. If None, the images aren't cached and loaded from the .slp file on each access.
cache_img_path

Path to save the .jpg files. If None, current working dir is used.
use_existing_imgs

Use existing imgs/ chunks in the cache_img_path.
rank

Indicates the rank of the process. Used during distributed training to ensure that image storage to disk occurs only once across all workers.

Methods:

Name Description
__getitem__

Return dict with image, confmaps and pafs for given index.
__init__

Initialize class attributes.
Source code in sleap_nn/data/custom_datasets.py 
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class BottomUpMultiClassDataset(BaseDataset):
    """Dataset class for bottom-up ID models.

    Attributes:
        labels: Source `sio.Labels` object.
        max_stride: Scalar integer specifying the maximum stride that the image must be
            divisible by.
        class_map_threshold: Minimum confidence map value below which map values will be
            replaced with zeros.
        user_instances_only: `True` if only user labeled instances should be used for training. If `False`,
            both user labeled and predicted instances would be used.
        ensure_rgb: (bool) True if the input image should have 3 channels (RGB image). If input has only one
        channel when this is set to `True`, then the images from single-channel
        is replicated along the channel axis. If the image has three channels and this is set to False, then we retain the three channels. Default: `False`.
        ensure_grayscale: (bool) True if the input image should only have a single channel. If input has three channels (RGB) and this
        is set to True, then we convert the image to grayscale (single-channel)
        image. If the source image has only one channel and this is set to False, then we retain the single channel input. Default: `False`.
        intensity_aug: Intensity augmentation configuration. Can be:
            - String: One of ['uniform_noise', 'gaussian_noise', 'contrast', 'brightness']
            - List of strings: Multiple intensity augmentations from the allowed values
            - Dictionary: Custom intensity configuration
            - None: No intensity augmentation applied
        geometric_aug: Geometric augmentation configuration. Can be:
            - String: One of ['rotation', 'scale', 'translate', 'erase_scale', 'mixup']
            - List of strings: Multiple geometric augmentations from the allowed values
            - Dictionary: Custom geometric configuration
            - None: No geometric augmentation applied
        scale: Factor to resize the image dimensions by, specified as a float. Default: 1.0.
        apply_aug: `True` if augmentations should be applied to the data pipeline,
            else `False`. Default: `False`.
        max_hw: Maximum height and width of images across the labels file. If `max_height` and
           `max_width` in the config is None, then `max_hw` is used (computed with
            `sleap_nn.data.providers.get_max_height_width`). Else the values in the config
            are used.
        confmap_head_config: DictConfig object with all the keys in the `head_config` section.
            (required keys: `sigma`, `output_stride` and `anchor_part` depending on the model type ).
        class_maps_head_config: DictConfig object with all the keys in the `head_config` section
            (required keys: `sigma`, `output_stride` and `classes`)
            for class maps.
        cache_img: String to indicate which caching to use: `memory` or `disk`. If `None`,
            the images aren't cached and loaded from the `.slp` file on each access.
        cache_img_path: Path to save the `.jpg` files. If `None`, current working dir is used.
        use_existing_imgs: Use existing imgs/ chunks in the `cache_img_path`.
        rank: Indicates the rank of the process. Used during distributed training to ensure that image storage to
            disk occurs only once across all workers.
    """

    def __init__(
        self,
        labels: List[sio.Labels],
        confmap_head_config: DictConfig,
        class_maps_head_config: DictConfig,
        max_stride: int,
        class_map_threshold: float = 0.2,
        user_instances_only: bool = True,
        ensure_rgb: bool = False,
        ensure_grayscale: bool = False,
        intensity_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
        geometric_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
        scale: float = 1.0,
        apply_aug: bool = False,
        max_hw: Tuple[Optional[int]] = (None, None),
        cache_img: Optional[str] = None,
        cache_img_path: Optional[str] = None,
        use_existing_imgs: bool = False,
        rank: Optional[int] = None,
    ) -> None:
        """Initialize class attributes."""
        super().__init__(
            labels=labels,
            max_stride=max_stride,
            user_instances_only=user_instances_only,
            ensure_rgb=ensure_rgb,
            ensure_grayscale=ensure_grayscale,
            intensity_aug=intensity_aug,
            geometric_aug=geometric_aug,
            scale=scale,
            apply_aug=apply_aug,
            max_hw=max_hw,
            cache_img=cache_img,
            cache_img_path=cache_img_path,
            use_existing_imgs=use_existing_imgs,
            rank=rank,
        )
        self.confmap_head_config = confmap_head_config
        self.class_maps_head_config = class_maps_head_config
        self.tracks = []
        for train_label in self.labels:
            self.tracks.extend([x.name for x in train_label.tracks if x is not None])
        self.tracks = list(set(self.tracks))
        self.class_map_threshold = class_map_threshold

    def __getitem__(self, index) -> Dict:
        """Return dict with image, confmaps and pafs for given index."""
        (labels_idx, lf_idx) = self.lf_idx_list[index]

        lf = self.labels[labels_idx][lf_idx]

        # load the img
        if self.cache_img is not None:
            if self.cache_img == "disk":
                img = np.array(
                    Image.open(
                        f"{self.cache_img_path}/sample_{labels_idx}_{lf_idx}.jpg"
                    )
                )
            elif self.cache_img == "memory":
                img = self.cache[(labels_idx, lf_idx)].copy()

        else:  # load from slp file if not cached
            img = lf.image

        if img.ndim == 2:
            img = np.expand_dims(img, axis=2)

        video_idx = self._get_video_idx(lf, labels_idx)

        # get dict
        sample = process_lf(
            lf,
            video_idx=video_idx,
            max_instances=self.max_instances,
            user_instances_only=self.user_instances_only,
        )

        sample["track_ids"] = torch.Tensor(
            [
                (
                    self.tracks.index(lf.instances[idx].track.name)
                    if lf.instances[idx].track is not None
                    else -1
                )
                for idx in range(sample["num_instances"])
            ]
        ).to(torch.int32)

        sample["num_tracks"] = torch.tensor(len(self.tracks), dtype=torch.int32)

        # apply normalization
        sample["image"] = apply_normalization(sample["image"])

        if self.ensure_rgb:
            sample["image"] = convert_to_rgb(sample["image"])
        elif self.ensure_grayscale:
            sample["image"] = convert_to_grayscale(sample["image"])

        # size matcher
        sample["image"], eff_scale = apply_sizematcher(
            sample["image"],
            max_height=self.max_hw[0],
            max_width=self.max_hw[1],
        )
        sample["instances"] = sample["instances"] * eff_scale
        sample["eff_scale"] = torch.tensor(eff_scale, dtype=torch.float32)

        # resize image
        sample["image"], sample["instances"] = apply_resizer(
            sample["image"],
            sample["instances"],
            scale=self.scale,
        )

        # Pad the image (if needed) according max stride
        sample["image"] = apply_pad_to_stride(
            sample["image"], max_stride=self.max_stride
        )

        # apply augmentation
        if self.apply_aug:
            if self.intensity_aug is not None:
                sample["image"], sample["instances"] = apply_intensity_augmentation(
                    sample["image"],
                    sample["instances"],
                    **self.intensity_aug,
                )

            if self.geometric_aug is not None:
                sample["image"], sample["instances"] = apply_geometric_augmentation(
                    sample["image"],
                    sample["instances"],
                    **self.geometric_aug,
                )

        img_hw = sample["image"].shape[-2:]

        # Generate confidence maps
        confidence_maps = generate_multiconfmaps(
            sample["instances"],
            img_hw=img_hw,
            num_instances=sample["num_instances"],
            sigma=self.confmap_head_config.sigma,
            output_stride=self.confmap_head_config.output_stride,
            is_centroids=False,
        )

        # class maps
        class_maps = generate_class_maps(
            instances=sample["instances"],
            img_hw=img_hw,
            num_instances=sample["num_instances"],
            class_inds=sample["track_ids"],
            num_tracks=sample["num_tracks"],
            class_map_threshold=self.class_map_threshold,
            sigma=self.class_maps_head_config.sigma,
            output_stride=self.class_maps_head_config.output_stride,
            is_centroids=False,
        )

        sample["confidence_maps"] = confidence_maps
        sample["class_maps"] = class_maps
        sample["labels_idx"] = labels_idx

        return sample

__getitem__(index)

Return dict with image, confmaps and pafs for given index.

Source code in sleap_nn/data/custom_datasets.py 
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def __getitem__(self, index) -> Dict:
    """Return dict with image, confmaps and pafs for given index."""
    (labels_idx, lf_idx) = self.lf_idx_list[index]

    lf = self.labels[labels_idx][lf_idx]

    # load the img
    if self.cache_img is not None:
        if self.cache_img == "disk":
            img = np.array(
                Image.open(
                    f"{self.cache_img_path}/sample_{labels_idx}_{lf_idx}.jpg"
                )
            )
        elif self.cache_img == "memory":
            img = self.cache[(labels_idx, lf_idx)].copy()

    else:  # load from slp file if not cached
        img = lf.image

    if img.ndim == 2:
        img = np.expand_dims(img, axis=2)

    video_idx = self._get_video_idx(lf, labels_idx)

    # get dict
    sample = process_lf(
        lf,
        video_idx=video_idx,
        max_instances=self.max_instances,
        user_instances_only=self.user_instances_only,
    )

    sample["track_ids"] = torch.Tensor(
        [
            (
                self.tracks.index(lf.instances[idx].track.name)
                if lf.instances[idx].track is not None
                else -1
            )
            for idx in range(sample["num_instances"])
        ]
    ).to(torch.int32)

    sample["num_tracks"] = torch.tensor(len(self.tracks), dtype=torch.int32)

    # apply normalization
    sample["image"] = apply_normalization(sample["image"])

    if self.ensure_rgb:
        sample["image"] = convert_to_rgb(sample["image"])
    elif self.ensure_grayscale:
        sample["image"] = convert_to_grayscale(sample["image"])

    # size matcher
    sample["image"], eff_scale = apply_sizematcher(
        sample["image"],
        max_height=self.max_hw[0],
        max_width=self.max_hw[1],
    )
    sample["instances"] = sample["instances"] * eff_scale
    sample["eff_scale"] = torch.tensor(eff_scale, dtype=torch.float32)

    # resize image
    sample["image"], sample["instances"] = apply_resizer(
        sample["image"],
        sample["instances"],
        scale=self.scale,
    )

    # Pad the image (if needed) according max stride
    sample["image"] = apply_pad_to_stride(
        sample["image"], max_stride=self.max_stride
    )

    # apply augmentation
    if self.apply_aug:
        if self.intensity_aug is not None:
            sample["image"], sample["instances"] = apply_intensity_augmentation(
                sample["image"],
                sample["instances"],
                **self.intensity_aug,
            )

        if self.geometric_aug is not None:
            sample["image"], sample["instances"] = apply_geometric_augmentation(
                sample["image"],
                sample["instances"],
                **self.geometric_aug,
            )

    img_hw = sample["image"].shape[-2:]

    # Generate confidence maps
    confidence_maps = generate_multiconfmaps(
        sample["instances"],
        img_hw=img_hw,
        num_instances=sample["num_instances"],
        sigma=self.confmap_head_config.sigma,
        output_stride=self.confmap_head_config.output_stride,
        is_centroids=False,
    )

    # class maps
    class_maps = generate_class_maps(
        instances=sample["instances"],
        img_hw=img_hw,
        num_instances=sample["num_instances"],
        class_inds=sample["track_ids"],
        num_tracks=sample["num_tracks"],
        class_map_threshold=self.class_map_threshold,
        sigma=self.class_maps_head_config.sigma,
        output_stride=self.class_maps_head_config.output_stride,
        is_centroids=False,
    )

    sample["confidence_maps"] = confidence_maps
    sample["class_maps"] = class_maps
    sample["labels_idx"] = labels_idx

    return sample

__init__(labels, confmap_head_config, class_maps_head_config, max_stride, class_map_threshold=0.2, user_instances_only=True, ensure_rgb=False, ensure_grayscale=False, intensity_aug=None, geometric_aug=None, scale=1.0, apply_aug=False, max_hw=(None, None), cache_img=None, cache_img_path=None, use_existing_imgs=False, rank=None)

Initialize class attributes.

Source code in sleap_nn/data/custom_datasets.py 
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def __init__(
    self,
    labels: List[sio.Labels],
    confmap_head_config: DictConfig,
    class_maps_head_config: DictConfig,
    max_stride: int,
    class_map_threshold: float = 0.2,
    user_instances_only: bool = True,
    ensure_rgb: bool = False,
    ensure_grayscale: bool = False,
    intensity_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
    geometric_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
    scale: float = 1.0,
    apply_aug: bool = False,
    max_hw: Tuple[Optional[int]] = (None, None),
    cache_img: Optional[str] = None,
    cache_img_path: Optional[str] = None,
    use_existing_imgs: bool = False,
    rank: Optional[int] = None,
) -> None:
    """Initialize class attributes."""
    super().__init__(
        labels=labels,
        max_stride=max_stride,
        user_instances_only=user_instances_only,
        ensure_rgb=ensure_rgb,
        ensure_grayscale=ensure_grayscale,
        intensity_aug=intensity_aug,
        geometric_aug=geometric_aug,
        scale=scale,
        apply_aug=apply_aug,
        max_hw=max_hw,
        cache_img=cache_img,
        cache_img_path=cache_img_path,
        use_existing_imgs=use_existing_imgs,
        rank=rank,
    )
    self.confmap_head_config = confmap_head_config
    self.class_maps_head_config = class_maps_head_config
    self.tracks = []
    for train_label in self.labels:
        self.tracks.extend([x.name for x in train_label.tracks if x is not None])
    self.tracks = list(set(self.tracks))
    self.class_map_threshold = class_map_threshold

CenteredInstanceDataset

Bases: BaseDataset

Dataset class for instance-centered confidence map models.

Attributes:

Name Type Description
labels

Source sio.Labels object.
max_stride

Scalar integer specifying the maximum stride that the image must be divisible by.
anchor_ind

Index of the node to use as the anchor point, based on its index in the ordered list of skeleton nodes.
user_instances_only

True if only user labeled instances should be used for training. If False, both user labeled and predicted instances would be used.
ensure_rgb

(bool) True if the input image should have 3 channels (RGB image). If input has only one
is replicated along the channel axis. If the image has three channels and this is set to False, then we retain the three channels. Default

False.
ensure_grayscale

(bool) True if the input image should only have a single channel. If input has three channels (RGB) and this
image. If the source image has only one channel and this is set to False, then we retain the single channel input. Default

False.
intensity_aug

Intensity augmentation configuration. Can be: - String: One of ['uniform_noise', 'gaussian_noise', 'contrast', 'brightness'] - List of strings: Multiple intensity augmentations from the allowed values - Dictionary: Custom intensity configuration - None: No intensity augmentation applied
geometric_aug

Geometric augmentation configuration. Can be: - String: One of ['rotation', 'scale', 'translate', 'erase_scale', 'mixup'] - List of strings: Multiple geometric augmentations from the allowed values - Dictionary: Custom geometric configuration - None: No geometric augmentation applied
scale

Factor to resize the image dimensions by, specified as a float. Default: 1.0.
apply_aug

True if augmentations should be applied to the data pipeline, else False. Default: False.
max_hw

Maximum height and width of images across the labels file. If max_height and max_width in the config is None, then max_hw is used (computed with sleap_nn.data.providers.get_max_height_width). Else the values in the config are used.
cache_img

String to indicate which caching to use: memory or disk. If None, the images aren't cached and loaded from the .slp file on each access.
cache_img_path

Path to save the .jpg files. If None, current working dir is used.
use_existing_imgs

Use existing imgs/ chunks in the cache_img_path.
crop_size

Crop size of each instance for centered-instance model.
rank

Indicates the rank of the process. Used during distributed training to ensure that image storage to disk occurs only once across all workers.

Note: If scale is provided for centered-instance model, the images are cropped out from the scaled image with the given crop size.

Methods:

Name Description
__getitem__

Return dict with cropped image and confmaps of instance for given index.
__init__

Initialize class attributes.
__len__

Return number of instances in the labels object.
Source code in sleap_nn/data/custom_datasets.py 
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class CenteredInstanceDataset(BaseDataset):
    """Dataset class for instance-centered confidence map models.

    Attributes:
        labels: Source `sio.Labels` object.
        max_stride: Scalar integer specifying the maximum stride that the image must be
            divisible by.
        anchor_ind: Index of the node to use as the anchor point, based on its index in the
            ordered list of skeleton nodes.
        user_instances_only: `True` if only user labeled instances should be used for training. If `False`,
            both user labeled and predicted instances would be used.
        ensure_rgb: (bool) True if the input image should have 3 channels (RGB image). If input has only one
        channel when this is set to `True`, then the images from single-channel
        is replicated along the channel axis. If the image has three channels and this is set to False, then we retain the three channels. Default: `False`.
        ensure_grayscale: (bool) True if the input image should only have a single channel. If input has three channels (RGB) and this
        is set to True, then we convert the image to grayscale (single-channel)
        image. If the source image has only one channel and this is set to False, then we retain the single channel input. Default: `False`.
        intensity_aug: Intensity augmentation configuration. Can be:
            - String: One of ['uniform_noise', 'gaussian_noise', 'contrast', 'brightness']
            - List of strings: Multiple intensity augmentations from the allowed values
            - Dictionary: Custom intensity configuration
            - None: No intensity augmentation applied
        geometric_aug: Geometric augmentation configuration. Can be:
            - String: One of ['rotation', 'scale', 'translate', 'erase_scale', 'mixup']
            - List of strings: Multiple geometric augmentations from the allowed values
            - Dictionary: Custom geometric configuration
            - None: No geometric augmentation applied
        scale: Factor to resize the image dimensions by, specified as a float. Default: 1.0.
        apply_aug: `True` if augmentations should be applied to the data pipeline,
            else `False`. Default: `False`.
        max_hw: Maximum height and width of images across the labels file. If `max_height` and
           `max_width` in the config is None, then `max_hw` is used (computed with
            `sleap_nn.data.providers.get_max_height_width`). Else the values in the config
            are used.
        cache_img: String to indicate which caching to use: `memory` or `disk`. If `None`,
            the images aren't cached and loaded from the `.slp` file on each access.
        cache_img_path: Path to save the `.jpg` files. If `None`, current working dir is used.
        use_existing_imgs: Use existing imgs/ chunks in the `cache_img_path`.
        crop_size: Crop size of each instance for centered-instance model.
        rank: Indicates the rank of the process. Used during distributed training to ensure that image storage to
            disk occurs only once across all workers.

    Note: If scale is provided for centered-instance model, the images are cropped out
    from the scaled image with the given crop size.
    """

    def __init__(
        self,
        labels: List[sio.Labels],
        crop_size: int,
        confmap_head_config: DictConfig,
        max_stride: int,
        anchor_ind: Optional[int] = None,
        user_instances_only: bool = True,
        ensure_rgb: bool = False,
        ensure_grayscale: bool = False,
        intensity_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
        geometric_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
        scale: float = 1.0,
        apply_aug: bool = False,
        max_hw: Tuple[Optional[int]] = (None, None),
        cache_img: Optional[str] = None,
        cache_img_path: Optional[str] = None,
        use_existing_imgs: bool = False,
        rank: Optional[int] = None,
    ) -> None:
        """Initialize class attributes."""
        super().__init__(
            labels=labels,
            max_stride=max_stride,
            user_instances_only=user_instances_only,
            ensure_rgb=ensure_rgb,
            ensure_grayscale=ensure_grayscale,
            intensity_aug=intensity_aug,
            geometric_aug=geometric_aug,
            scale=scale,
            apply_aug=apply_aug,
            max_hw=max_hw,
            cache_img=cache_img,
            cache_img_path=cache_img_path,
            use_existing_imgs=use_existing_imgs,
            rank=rank,
        )
        self.crop_size = crop_size
        self.anchor_ind = anchor_ind
        self.confmap_head_config = confmap_head_config
        self.instance_idx_list = self._get_instance_idx_list()
        self.cache_lf = [None, None]

    def _get_instance_idx_list(self) -> List[Tuple[int]]:
        """Return list of tuples with indices of labelled frames and instances."""
        instance_idx_list = []
        for labels_idx, label in enumerate(self.labels):
            for lf_idx, lf in enumerate(label):
                # Filter to user instances
                if self.user_instances_only:
                    if lf.user_instances is not None and len(lf.user_instances) > 0:
                        lf.instances = lf.user_instances
                for inst_idx, inst in enumerate(lf.instances):
                    if not inst.is_empty:  # filter all NaN instances.
                        instance_idx_list.append((labels_idx, lf_idx, inst_idx))
        return instance_idx_list

    def __len__(self) -> int:
        """Return number of instances in the labels object."""
        return len(self.instance_idx_list)

    def __getitem__(self, index) -> Dict:
        """Return dict with cropped image and confmaps of instance for given index."""
        labels_idx, lf_idx, inst_idx = self.instance_idx_list[index]
        lf = self.labels[labels_idx][lf_idx]

        if lf_idx == self.cache_lf[0]:
            img = self.cache_lf[1]
        else:
            # load the img
            if self.cache_img is not None:
                if self.cache_img == "disk":
                    img = np.array(
                        Image.open(
                            f"{self.cache_img_path}/sample_{labels_idx}_{lf_idx}.jpg"
                        )
                    )
                elif self.cache_img == "memory":
                    img = self.cache[(labels_idx, lf_idx)].copy()

            else:  # load from slp file if not cached
                img = lf.image  # TODO: doesn't work when num_workers > 0

            if img.ndim == 2:
                img = np.expand_dims(img, axis=2)

            self.cache_lf = [lf_idx, img]

        video_idx = self._get_video_idx(lf, labels_idx)

        image = np.transpose(img, (2, 0, 1))  # HWC -> CHW

        instances = []
        for inst in lf:
            instances.append(inst.numpy())
        instances = np.stack(instances, axis=0)

        # Add singleton time dimension for single frames.
        image = np.expand_dims(image, axis=0)  # (n_samples=1, C, H, W)
        instances = np.expand_dims(
            instances, axis=0
        )  # (n_samples=1, num_instances, num_nodes, 2)

        instances = torch.from_numpy(instances.astype("float32"))
        image = torch.from_numpy(image.copy())

        num_instances, _ = instances.shape[1:3]
        orig_img_height, orig_img_width = image.shape[-2:]

        instances = instances[:, inst_idx]

        # apply normalization
        image = apply_normalization(image)

        if self.ensure_rgb:
            image = convert_to_rgb(image)
        elif self.ensure_grayscale:
            image = convert_to_grayscale(image)

        # size matcher
        image, eff_scale = apply_sizematcher(
            image,
            max_height=self.max_hw[0],
            max_width=self.max_hw[1],
        )
        instances = instances * eff_scale

        # resize image
        image, instances = apply_resizer(
            image,
            instances,
            scale=self.scale,
        )

        # get the centroids based on the anchor idx
        centroids = generate_centroids(instances, anchor_ind=self.anchor_ind)

        instance, centroid = instances[0], centroids[0]  # (n_samples=1)

        crop_size = np.array([self.crop_size, self.crop_size]) * np.sqrt(
            2
        )  # crop extra for rotation augmentation
        crop_size = crop_size.astype(np.int32).tolist()

        sample = generate_crops(image, instance, centroid, crop_size)

        sample["frame_idx"] = torch.tensor(lf.frame_idx, dtype=torch.int32)
        sample["video_idx"] = torch.tensor(video_idx, dtype=torch.int32)
        sample["num_instances"] = num_instances
        sample["orig_size"] = torch.Tensor([orig_img_height, orig_img_width])
        sample["eff_scale"] = torch.tensor(eff_scale, dtype=torch.float32)

        # apply augmentation
        if self.apply_aug:
            if self.intensity_aug is not None:
                (
                    sample["instance_image"],
                    sample["instance"],
                ) = apply_intensity_augmentation(
                    sample["instance_image"],
                    sample["instance"],
                    **self.intensity_aug,
                )

            if self.geometric_aug is not None:
                (
                    sample["instance_image"],
                    sample["instance"],
                ) = apply_geometric_augmentation(
                    sample["instance_image"],
                    sample["instance"],
                    **self.geometric_aug,
                )

        # re-crop to original crop size
        sample["instance_bbox"] = torch.unsqueeze(
            make_centered_bboxes(sample["centroid"][0], self.crop_size, self.crop_size),
            0,
        )  # (n_samples=1, 4, 2)

        sample["instance_image"] = crop_and_resize(
            sample["instance_image"],
            boxes=sample["instance_bbox"],
            size=(self.crop_size, self.crop_size),
        )
        point = sample["instance_bbox"][0][0]
        center_instance = sample["instance"] - point
        centered_centroid = sample["centroid"] - point

        sample["instance"] = center_instance  # (n_samples=1, n_nodes, 2)
        sample["centroid"] = centered_centroid  # (n_samples=1, 2)

        # Pad the image (if needed) according max stride
        sample["instance_image"] = apply_pad_to_stride(
            sample["instance_image"], max_stride=self.max_stride
        )

        img_hw = sample["instance_image"].shape[-2:]

        # Generate confidence maps
        confidence_maps = generate_confmaps(
            sample["instance"],
            img_hw=img_hw,
            sigma=self.confmap_head_config.sigma,
            output_stride=self.confmap_head_config.output_stride,
        )

        sample["confidence_maps"] = confidence_maps
        sample["labels_idx"] = labels_idx

        return sample

__getitem__(index)

Return dict with cropped image and confmaps of instance for given index.

Source code in sleap_nn/data/custom_datasets.py 
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def __getitem__(self, index) -> Dict:
    """Return dict with cropped image and confmaps of instance for given index."""
    labels_idx, lf_idx, inst_idx = self.instance_idx_list[index]
    lf = self.labels[labels_idx][lf_idx]

    if lf_idx == self.cache_lf[0]:
        img = self.cache_lf[1]
    else:
        # load the img
        if self.cache_img is not None:
            if self.cache_img == "disk":
                img = np.array(
                    Image.open(
                        f"{self.cache_img_path}/sample_{labels_idx}_{lf_idx}.jpg"
                    )
                )
            elif self.cache_img == "memory":
                img = self.cache[(labels_idx, lf_idx)].copy()

        else:  # load from slp file if not cached
            img = lf.image  # TODO: doesn't work when num_workers > 0

        if img.ndim == 2:
            img = np.expand_dims(img, axis=2)

        self.cache_lf = [lf_idx, img]

    video_idx = self._get_video_idx(lf, labels_idx)

    image = np.transpose(img, (2, 0, 1))  # HWC -> CHW

    instances = []
    for inst in lf:
        instances.append(inst.numpy())
    instances = np.stack(instances, axis=0)

    # Add singleton time dimension for single frames.
    image = np.expand_dims(image, axis=0)  # (n_samples=1, C, H, W)
    instances = np.expand_dims(
        instances, axis=0
    )  # (n_samples=1, num_instances, num_nodes, 2)

    instances = torch.from_numpy(instances.astype("float32"))
    image = torch.from_numpy(image.copy())

    num_instances, _ = instances.shape[1:3]
    orig_img_height, orig_img_width = image.shape[-2:]

    instances = instances[:, inst_idx]

    # apply normalization
    image = apply_normalization(image)

    if self.ensure_rgb:
        image = convert_to_rgb(image)
    elif self.ensure_grayscale:
        image = convert_to_grayscale(image)

    # size matcher
    image, eff_scale = apply_sizematcher(
        image,
        max_height=self.max_hw[0],
        max_width=self.max_hw[1],
    )
    instances = instances * eff_scale

    # resize image
    image, instances = apply_resizer(
        image,
        instances,
        scale=self.scale,
    )

    # get the centroids based on the anchor idx
    centroids = generate_centroids(instances, anchor_ind=self.anchor_ind)

    instance, centroid = instances[0], centroids[0]  # (n_samples=1)

    crop_size = np.array([self.crop_size, self.crop_size]) * np.sqrt(
        2
    )  # crop extra for rotation augmentation
    crop_size = crop_size.astype(np.int32).tolist()

    sample = generate_crops(image, instance, centroid, crop_size)

    sample["frame_idx"] = torch.tensor(lf.frame_idx, dtype=torch.int32)
    sample["video_idx"] = torch.tensor(video_idx, dtype=torch.int32)
    sample["num_instances"] = num_instances
    sample["orig_size"] = torch.Tensor([orig_img_height, orig_img_width])
    sample["eff_scale"] = torch.tensor(eff_scale, dtype=torch.float32)

    # apply augmentation
    if self.apply_aug:
        if self.intensity_aug is not None:
            (
                sample["instance_image"],
                sample["instance"],
            ) = apply_intensity_augmentation(
                sample["instance_image"],
                sample["instance"],
                **self.intensity_aug,
            )

        if self.geometric_aug is not None:
            (
                sample["instance_image"],
                sample["instance"],
            ) = apply_geometric_augmentation(
                sample["instance_image"],
                sample["instance"],
                **self.geometric_aug,
            )

    # re-crop to original crop size
    sample["instance_bbox"] = torch.unsqueeze(
        make_centered_bboxes(sample["centroid"][0], self.crop_size, self.crop_size),
        0,
    )  # (n_samples=1, 4, 2)

    sample["instance_image"] = crop_and_resize(
        sample["instance_image"],
        boxes=sample["instance_bbox"],
        size=(self.crop_size, self.crop_size),
    )
    point = sample["instance_bbox"][0][0]
    center_instance = sample["instance"] - point
    centered_centroid = sample["centroid"] - point

    sample["instance"] = center_instance  # (n_samples=1, n_nodes, 2)
    sample["centroid"] = centered_centroid  # (n_samples=1, 2)

    # Pad the image (if needed) according max stride
    sample["instance_image"] = apply_pad_to_stride(
        sample["instance_image"], max_stride=self.max_stride
    )

    img_hw = sample["instance_image"].shape[-2:]

    # Generate confidence maps
    confidence_maps = generate_confmaps(
        sample["instance"],
        img_hw=img_hw,
        sigma=self.confmap_head_config.sigma,
        output_stride=self.confmap_head_config.output_stride,
    )

    sample["confidence_maps"] = confidence_maps
    sample["labels_idx"] = labels_idx

    return sample

__init__(labels, crop_size, confmap_head_config, max_stride, anchor_ind=None, user_instances_only=True, ensure_rgb=False, ensure_grayscale=False, intensity_aug=None, geometric_aug=None, scale=1.0, apply_aug=False, max_hw=(None, None), cache_img=None, cache_img_path=None, use_existing_imgs=False, rank=None)

Initialize class attributes.

Source code in sleap_nn/data/custom_datasets.py 
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def __init__(
    self,
    labels: List[sio.Labels],
    crop_size: int,
    confmap_head_config: DictConfig,
    max_stride: int,
    anchor_ind: Optional[int] = None,
    user_instances_only: bool = True,
    ensure_rgb: bool = False,
    ensure_grayscale: bool = False,
    intensity_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
    geometric_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
    scale: float = 1.0,
    apply_aug: bool = False,
    max_hw: Tuple[Optional[int]] = (None, None),
    cache_img: Optional[str] = None,
    cache_img_path: Optional[str] = None,
    use_existing_imgs: bool = False,
    rank: Optional[int] = None,
) -> None:
    """Initialize class attributes."""
    super().__init__(
        labels=labels,
        max_stride=max_stride,
        user_instances_only=user_instances_only,
        ensure_rgb=ensure_rgb,
        ensure_grayscale=ensure_grayscale,
        intensity_aug=intensity_aug,
        geometric_aug=geometric_aug,
        scale=scale,
        apply_aug=apply_aug,
        max_hw=max_hw,
        cache_img=cache_img,
        cache_img_path=cache_img_path,
        use_existing_imgs=use_existing_imgs,
        rank=rank,
    )
    self.crop_size = crop_size
    self.anchor_ind = anchor_ind
    self.confmap_head_config = confmap_head_config
    self.instance_idx_list = self._get_instance_idx_list()
    self.cache_lf = [None, None]

__len__()

Return number of instances in the labels object.

Source code in sleap_nn/data/custom_datasets.py 
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def __len__(self) -> int:
    """Return number of instances in the labels object."""
    return len(self.instance_idx_list)

CentroidDataset

Bases: BaseDataset

Dataset class for centroid models.

Attributes:

Name Type Description
labels

Source sio.Labels object.
max_stride

Scalar integer specifying the maximum stride that the image must be divisible by.
anchor_ind

Index of the node to use as the anchor point, based on its index in the ordered list of skeleton nodes.
user_instances_only

True if only user labeled instances should be used for training. If False, both user labeled and predicted instances would be used.
ensure_rgb

(bool) True if the input image should have 3 channels (RGB image). If input has only one
is replicated along the channel axis. If the image has three channels and this is set to False, then we retain the three channels. Default

False.
ensure_grayscale

(bool) True if the input image should only have a single channel. If input has three channels (RGB) and this
image. If the source image has only one channel and this is set to False, then we retain the single channel input. Default

False.
intensity_aug

Intensity augmentation configuration. Can be: - String: One of ['uniform_noise', 'gaussian_noise', 'contrast', 'brightness'] - List of strings: Multiple intensity augmentations from the allowed values - Dictionary: Custom intensity configuration - None: No intensity augmentation applied
geometric_aug

Geometric augmentation configuration. Can be: - String: One of ['rotation', 'scale', 'translate', 'erase_scale', 'mixup'] - List of strings: Multiple geometric augmentations from the allowed values - Dictionary: Custom geometric configuration - None: No geometric augmentation applied
scale

Factor to resize the image dimensions by, specified as a float. Default: 1.0.
apply_aug

True if augmentations should be applied to the data pipeline, else False. Default: False.
max_hw

Maximum height and width of images across the labels file. If max_height and max_width in the config is None, then max_hw is used (computed with sleap_nn.data.providers.get_max_height_width). Else the values in the config are used.
cache_img

String to indicate which caching to use: memory or disk. If None, the images aren't cached and loaded from the .slp file on each access.
cache_img_path

Path to save the .jpg files. If None, current working dir is used.
use_existing_imgs

Use existing imgs/ chunks in the cache_img_path.
confmap_head_config

DictConfig object with all the keys in the head_config section.
(required keys

sigma, output_stride and anchor_part depending on the model type ).
rank

Indicates the rank of the process. Used during distributed training to ensure that image storage to disk occurs only once across all workers.

Methods:

Name Description
__getitem__

Return dict with image and confmaps for centroids for given index.
__init__

Initialize class attributes.
Source code in sleap_nn/data/custom_datasets.py 
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class CentroidDataset(BaseDataset):
    """Dataset class for centroid models.

    Attributes:
        labels: Source `sio.Labels` object.
        max_stride: Scalar integer specifying the maximum stride that the image must be
            divisible by.
        anchor_ind: Index of the node to use as the anchor point, based on its index in the
            ordered list of skeleton nodes.
        user_instances_only: `True` if only user labeled instances should be used for training. If `False`,
            both user labeled and predicted instances would be used.
        ensure_rgb: (bool) True if the input image should have 3 channels (RGB image). If input has only one
        channel when this is set to `True`, then the images from single-channel
        is replicated along the channel axis. If the image has three channels and this is set to False, then we retain the three channels. Default: `False`.
        ensure_grayscale: (bool) True if the input image should only have a single channel. If input has three channels (RGB) and this
        is set to True, then we convert the image to grayscale (single-channel)
        image. If the source image has only one channel and this is set to False, then we retain the single channel input. Default: `False`.
        intensity_aug: Intensity augmentation configuration. Can be:
            - String: One of ['uniform_noise', 'gaussian_noise', 'contrast', 'brightness']
            - List of strings: Multiple intensity augmentations from the allowed values
            - Dictionary: Custom intensity configuration
            - None: No intensity augmentation applied
        geometric_aug: Geometric augmentation configuration. Can be:
            - String: One of ['rotation', 'scale', 'translate', 'erase_scale', 'mixup']
            - List of strings: Multiple geometric augmentations from the allowed values
            - Dictionary: Custom geometric configuration
            - None: No geometric augmentation applied
        scale: Factor to resize the image dimensions by, specified as a float. Default: 1.0.
        apply_aug: `True` if augmentations should be applied to the data pipeline,
            else `False`. Default: `False`.
        max_hw: Maximum height and width of images across the labels file. If `max_height` and
           `max_width` in the config is None, then `max_hw` is used (computed with
            `sleap_nn.data.providers.get_max_height_width`). Else the values in the config
            are used.
        cache_img: String to indicate which caching to use: `memory` or `disk`. If `None`,
            the images aren't cached and loaded from the `.slp` file on each access.
        cache_img_path: Path to save the `.jpg` files. If `None`, current working dir is used.
        use_existing_imgs: Use existing imgs/ chunks in the `cache_img_path`.
        confmap_head_config: DictConfig object with all the keys in the `head_config` section.
        (required keys: `sigma`, `output_stride` and `anchor_part` depending on the model type ).
        rank: Indicates the rank of the process. Used during distributed training to ensure that image storage to
            disk occurs only once across all workers.
    """

    def __init__(
        self,
        labels: List[sio.Labels],
        confmap_head_config: DictConfig,
        max_stride: int,
        anchor_ind: Optional[int] = None,
        user_instances_only: bool = True,
        ensure_rgb: bool = False,
        ensure_grayscale: bool = False,
        intensity_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
        geometric_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
        scale: float = 1.0,
        apply_aug: bool = False,
        max_hw: Tuple[Optional[int]] = (None, None),
        cache_img: Optional[str] = None,
        cache_img_path: Optional[str] = None,
        use_existing_imgs: bool = False,
        rank: Optional[int] = None,
    ) -> None:
        """Initialize class attributes."""
        super().__init__(
            labels=labels,
            max_stride=max_stride,
            user_instances_only=user_instances_only,
            ensure_rgb=ensure_rgb,
            ensure_grayscale=ensure_grayscale,
            intensity_aug=intensity_aug,
            geometric_aug=geometric_aug,
            scale=scale,
            apply_aug=apply_aug,
            max_hw=max_hw,
            cache_img=cache_img,
            cache_img_path=cache_img_path,
            use_existing_imgs=use_existing_imgs,
            rank=rank,
        )
        self.anchor_ind = anchor_ind
        self.confmap_head_config = confmap_head_config

    def __getitem__(self, index) -> Dict:
        """Return dict with image and confmaps for centroids for given index."""
        (labels_idx, lf_idx) = self.lf_idx_list[index]

        lf = self.labels[labels_idx][lf_idx]

        # load the img
        if self.cache_img is not None:
            if self.cache_img == "disk":
                img = np.array(
                    Image.open(
                        f"{self.cache_img_path}/sample_{labels_idx}_{lf_idx}.jpg"
                    )
                )
            elif self.cache_img == "memory":
                img = self.cache[(labels_idx, lf_idx)].copy()

        else:  # load from slp file if not cached
            img = lf.image

        if img.ndim == 2:
            img = np.expand_dims(img, axis=2)

        video_idx = self._get_video_idx(lf, labels_idx)

        # get dict
        sample = process_lf(
            lf,
            video_idx=video_idx,
            max_instances=self.max_instances,
            user_instances_only=self.user_instances_only,
        )

        # apply normalization
        sample["image"] = apply_normalization(sample["image"])

        if self.ensure_rgb:
            sample["image"] = convert_to_rgb(sample["image"])
        elif self.ensure_grayscale:
            sample["image"] = convert_to_grayscale(sample["image"])

        # size matcher
        sample["image"], eff_scale = apply_sizematcher(
            sample["image"],
            max_height=self.max_hw[0],
            max_width=self.max_hw[1],
        )
        sample["instances"] = sample["instances"] * eff_scale
        sample["eff_scale"] = torch.tensor(eff_scale, dtype=torch.float32)

        # resize image
        sample["image"], sample["instances"] = apply_resizer(
            sample["image"],
            sample["instances"],
            scale=self.scale,
        )

        # get the centroids based on the anchor idx
        centroids = generate_centroids(sample["instances"], anchor_ind=self.anchor_ind)

        sample["centroids"] = centroids

        # Pad the image (if needed) according max stride
        sample["image"] = apply_pad_to_stride(
            sample["image"], max_stride=self.max_stride
        )

        # apply augmentation
        if self.apply_aug:
            if self.intensity_aug is not None:
                sample["image"], sample["centroids"] = apply_intensity_augmentation(
                    sample["image"],
                    sample["centroids"],
                    **self.intensity_aug,
                )

            if self.geometric_aug is not None:
                sample["image"], sample["centroids"] = apply_geometric_augmentation(
                    sample["image"],
                    sample["centroids"],
                    **self.geometric_aug,
                )

        img_hw = sample["image"].shape[-2:]

        # Generate confidence maps
        confidence_maps = generate_multiconfmaps(
            sample["centroids"],
            img_hw=img_hw,
            num_instances=sample["num_instances"],
            sigma=self.confmap_head_config.sigma,
            output_stride=self.confmap_head_config.output_stride,
            is_centroids=True,
        )

        sample["centroids_confidence_maps"] = confidence_maps
        sample["labels_idx"] = labels_idx

        return sample

__getitem__(index)

Return dict with image and confmaps for centroids for given index.

Source code in sleap_nn/data/custom_datasets.py 
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def __getitem__(self, index) -> Dict:
    """Return dict with image and confmaps for centroids for given index."""
    (labels_idx, lf_idx) = self.lf_idx_list[index]

    lf = self.labels[labels_idx][lf_idx]

    # load the img
    if self.cache_img is not None:
        if self.cache_img == "disk":
            img = np.array(
                Image.open(
                    f"{self.cache_img_path}/sample_{labels_idx}_{lf_idx}.jpg"
                )
            )
        elif self.cache_img == "memory":
            img = self.cache[(labels_idx, lf_idx)].copy()

    else:  # load from slp file if not cached
        img = lf.image

    if img.ndim == 2:
        img = np.expand_dims(img, axis=2)

    video_idx = self._get_video_idx(lf, labels_idx)

    # get dict
    sample = process_lf(
        lf,
        video_idx=video_idx,
        max_instances=self.max_instances,
        user_instances_only=self.user_instances_only,
    )

    # apply normalization
    sample["image"] = apply_normalization(sample["image"])

    if self.ensure_rgb:
        sample["image"] = convert_to_rgb(sample["image"])
    elif self.ensure_grayscale:
        sample["image"] = convert_to_grayscale(sample["image"])

    # size matcher
    sample["image"], eff_scale = apply_sizematcher(
        sample["image"],
        max_height=self.max_hw[0],
        max_width=self.max_hw[1],
    )
    sample["instances"] = sample["instances"] * eff_scale
    sample["eff_scale"] = torch.tensor(eff_scale, dtype=torch.float32)

    # resize image
    sample["image"], sample["instances"] = apply_resizer(
        sample["image"],
        sample["instances"],
        scale=self.scale,
    )

    # get the centroids based on the anchor idx
    centroids = generate_centroids(sample["instances"], anchor_ind=self.anchor_ind)

    sample["centroids"] = centroids

    # Pad the image (if needed) according max stride
    sample["image"] = apply_pad_to_stride(
        sample["image"], max_stride=self.max_stride
    )

    # apply augmentation
    if self.apply_aug:
        if self.intensity_aug is not None:
            sample["image"], sample["centroids"] = apply_intensity_augmentation(
                sample["image"],
                sample["centroids"],
                **self.intensity_aug,
            )

        if self.geometric_aug is not None:
            sample["image"], sample["centroids"] = apply_geometric_augmentation(
                sample["image"],
                sample["centroids"],
                **self.geometric_aug,
            )

    img_hw = sample["image"].shape[-2:]

    # Generate confidence maps
    confidence_maps = generate_multiconfmaps(
        sample["centroids"],
        img_hw=img_hw,
        num_instances=sample["num_instances"],
        sigma=self.confmap_head_config.sigma,
        output_stride=self.confmap_head_config.output_stride,
        is_centroids=True,
    )

    sample["centroids_confidence_maps"] = confidence_maps
    sample["labels_idx"] = labels_idx

    return sample

__init__(labels, confmap_head_config, max_stride, anchor_ind=None, user_instances_only=True, ensure_rgb=False, ensure_grayscale=False, intensity_aug=None, geometric_aug=None, scale=1.0, apply_aug=False, max_hw=(None, None), cache_img=None, cache_img_path=None, use_existing_imgs=False, rank=None)

Initialize class attributes.

Source code in sleap_nn/data/custom_datasets.py 
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def __init__(
    self,
    labels: List[sio.Labels],
    confmap_head_config: DictConfig,
    max_stride: int,
    anchor_ind: Optional[int] = None,
    user_instances_only: bool = True,
    ensure_rgb: bool = False,
    ensure_grayscale: bool = False,
    intensity_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
    geometric_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
    scale: float = 1.0,
    apply_aug: bool = False,
    max_hw: Tuple[Optional[int]] = (None, None),
    cache_img: Optional[str] = None,
    cache_img_path: Optional[str] = None,
    use_existing_imgs: bool = False,
    rank: Optional[int] = None,
) -> None:
    """Initialize class attributes."""
    super().__init__(
        labels=labels,
        max_stride=max_stride,
        user_instances_only=user_instances_only,
        ensure_rgb=ensure_rgb,
        ensure_grayscale=ensure_grayscale,
        intensity_aug=intensity_aug,
        geometric_aug=geometric_aug,
        scale=scale,
        apply_aug=apply_aug,
        max_hw=max_hw,
        cache_img=cache_img,
        cache_img_path=cache_img_path,
        use_existing_imgs=use_existing_imgs,
        rank=rank,
    )
    self.anchor_ind = anchor_ind
    self.confmap_head_config = confmap_head_config

InfiniteDataLoader

Bases: DataLoader

Dataloader that reuses workers for infinite iteration.

This dataloader extends the PyTorch DataLoader to provide infinite recycling of workers, which improves efficiency for training loops that need to iterate through the dataset multiple times without recreating workers.

Attributes:

Name Type Description
batch_sampler _RepeatSampler

A sampler that repeats indefinitely.
iterator Iterator

The iterator from the parent DataLoader.
len_dataloader Optional[int]

Number of minibatches to be generated. If None, this is set to len(dataset)/batch_size.

Methods:

Name Description
__len__

Return the length of the batch sampler's sampler.
__iter__

Create a sampler that repeats indefinitely.
__del__

Ensure workers are properly terminated.
reset

Reset the iterator, useful when modifying dataset settings during training.

Examples:

Create an infinite dataloader for training

>>> dataset = CenteredInstanceDataset(...)
>>> dataloader = InfiniteDataLoader(dataset, batch_size=16, shuffle=True)
>>> for batch in dataloader:  # Infinite iteration
>>>     train_step(batch)

Source: https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/build.py

Source code in sleap_nn/data/custom_datasets.py 
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class InfiniteDataLoader(DataLoader):
    """Dataloader that reuses workers for infinite iteration.

    This dataloader extends the PyTorch DataLoader to provide infinite recycling of workers, which improves efficiency
    for training loops that need to iterate through the dataset multiple times without recreating workers.

    Attributes:
        batch_sampler (_RepeatSampler): A sampler that repeats indefinitely.
        iterator (Iterator): The iterator from the parent DataLoader.
        len_dataloader (Optional[int]): Number of minibatches to be generated. If `None`, this is set to len(dataset)/batch_size.

    Methods:
        __len__: Return the length of the batch sampler's sampler.
        __iter__: Create a sampler that repeats indefinitely.
        __del__: Ensure workers are properly terminated.
        reset: Reset the iterator, useful when modifying dataset settings during training.

    Examples:
        Create an infinite dataloader for training
        >>> dataset = CenteredInstanceDataset(...)
        >>> dataloader = InfiniteDataLoader(dataset, batch_size=16, shuffle=True)
        >>> for batch in dataloader:  # Infinite iteration
        >>>     train_step(batch)

    Source: https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/build.py
    """

    def __init__(self, len_dataloader: Optional[int] = None, *args: Any, **kwargs: Any):
        """Initialize the InfiniteDataLoader with the same arguments as DataLoader."""
        super().__init__(*args, **kwargs)
        object.__setattr__(self, "batch_sampler", _RepeatSampler(self.batch_sampler))
        self.iterator = super().__iter__()
        self.len_dataloader = len_dataloader

    def __len__(self) -> int:
        """Return the length of the batch sampler's sampler."""
        # set the len to required number of steps per epoch as Lightning Trainer
        # doesn't use the `__iter__` directly but instead uses the length to set
        # the number of steps per epoch. If this is just set to len(sampler), then
        # it only iterates through the samples in the dataset (and doesn't cycle through)
        # if the required steps per epoch is more than batches in dataset.
        return (
            self.len_dataloader
            if self.len_dataloader is not None
            else len(self.batch_sampler.sampler)
        )

    def __iter__(self) -> Iterator:
        """Create an iterator that yields indefinitely from the underlying iterator."""
        while True:
            yield next(self.iterator)

    def __del__(self):
        """Ensure that workers are properly terminated when the dataloader is deleted."""
        try:
            if not hasattr(self.iterator, "_workers"):
                return
            for w in self.iterator._workers:  # force terminate
                if w.is_alive():
                    w.terminate()
            self.iterator._shutdown_workers()  # cleanup
        except Exception:
            pass

    def reset(self):
        """Reset the iterator to allow modifications to the dataset during training."""
        self.iterator = self._get_iterator()

__del__()

Ensure that workers are properly terminated when the dataloader is deleted.

Source code in sleap_nn/data/custom_datasets.py 
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def __del__(self):
    """Ensure that workers are properly terminated when the dataloader is deleted."""
    try:
        if not hasattr(self.iterator, "_workers"):
            return
        for w in self.iterator._workers:  # force terminate
            if w.is_alive():
                w.terminate()
        self.iterator._shutdown_workers()  # cleanup
    except Exception:
        pass

__init__(len_dataloader=None, *args, **kwargs)

Initialize the InfiniteDataLoader with the same arguments as DataLoader.

Source code in sleap_nn/data/custom_datasets.py 
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def __init__(self, len_dataloader: Optional[int] = None, *args: Any, **kwargs: Any):
    """Initialize the InfiniteDataLoader with the same arguments as DataLoader."""
    super().__init__(*args, **kwargs)
    object.__setattr__(self, "batch_sampler", _RepeatSampler(self.batch_sampler))
    self.iterator = super().__iter__()
    self.len_dataloader = len_dataloader

__iter__()

Create an iterator that yields indefinitely from the underlying iterator.

Source code in sleap_nn/data/custom_datasets.py 
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def __iter__(self) -> Iterator:
    """Create an iterator that yields indefinitely from the underlying iterator."""
    while True:
        yield next(self.iterator)

__len__()

Return the length of the batch sampler's sampler.

Source code in sleap_nn/data/custom_datasets.py 
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def __len__(self) -> int:
    """Return the length of the batch sampler's sampler."""
    # set the len to required number of steps per epoch as Lightning Trainer
    # doesn't use the `__iter__` directly but instead uses the length to set
    # the number of steps per epoch. If this is just set to len(sampler), then
    # it only iterates through the samples in the dataset (and doesn't cycle through)
    # if the required steps per epoch is more than batches in dataset.
    return (
        self.len_dataloader
        if self.len_dataloader is not None
        else len(self.batch_sampler.sampler)
    )

reset()

Reset the iterator to allow modifications to the dataset during training.

Source code in sleap_nn/data/custom_datasets.py 
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def reset(self):
    """Reset the iterator to allow modifications to the dataset during training."""
    self.iterator = self._get_iterator()

SingleInstanceDataset

Bases: BaseDataset

Dataset class for single-instance models.

Attributes:

Name Type Description
labels

Source sio.Labels object.
max_stride

Scalar integer specifying the maximum stride that the image must be divisible by.
user_instances_only

True if only user labeled instances should be used for training. If False, both user labeled and predicted instances would be used.
ensure_rgb

(bool) True if the input image should have 3 channels (RGB image). If input has only one
is replicated along the channel axis. If the image has three channels and this is set to False, then we retain the three channels. Default

False.
ensure_grayscale

(bool) True if the input image should only have a single channel. If input has three channels (RGB) and this
image. If the source image has only one channel and this is set to False, then we retain the single channel input. Default

False.
intensity_aug

Intensity augmentation configuration. Can be: - String: One of ['uniform_noise', 'gaussian_noise', 'contrast', 'brightness'] - List of strings: Multiple intensity augmentations from the allowed values - Dictionary: Custom intensity configuration - None: No intensity augmentation applied
geometric_aug

Geometric augmentation configuration. Can be: - String: One of ['rotation', 'scale', 'translate', 'erase_scale', 'mixup'] - List of strings: Multiple geometric augmentations from the allowed values - Dictionary: Custom geometric configuration - None: No geometric augmentation applied
scale

Factor to resize the image dimensions by, specified as a float. Default: 1.0.
apply_aug

True if augmentations should be applied to the data pipeline, else False. Default: False.
max_hw

Maximum height and width of images across the labels file. If max_height and max_width in the config is None, then max_hw is used (computed with sleap_nn.data.providers.get_max_height_width). Else the values in the config are used.
cache_img

String to indicate which caching to use: memory or disk. If None, the images aren't cached and loaded from the .slp file on each access.
cache_img_path

Path to save the .jpg files. If None, current working dir is used.
use_existing_imgs

Use existing imgs/ chunks in the cache_img_path.
confmap_head_config

DictConfig object with all the keys in the head_config section.
(required keys

sigma, output_stride and anchor_part depending on the model type ).
rank

Indicates the rank of the process. Used during distributed training to ensure that image storage to disk occurs only once across all workers.

Methods:

Name Description
__getitem__

Return dict with image and confmaps for instance for given index.
__init__

Initialize class attributes.
Source code in sleap_nn/data/custom_datasets.py 
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class SingleInstanceDataset(BaseDataset):
    """Dataset class for single-instance models.

    Attributes:
        labels: Source `sio.Labels` object.
        max_stride: Scalar integer specifying the maximum stride that the image must be
            divisible by.
        user_instances_only: `True` if only user labeled instances should be used for training. If `False`,
            both user labeled and predicted instances would be used.
        ensure_rgb: (bool) True if the input image should have 3 channels (RGB image). If input has only one
        channel when this is set to `True`, then the images from single-channel
        is replicated along the channel axis. If the image has three channels and this is set to False, then we retain the three channels. Default: `False`.
        ensure_grayscale: (bool) True if the input image should only have a single channel. If input has three channels (RGB) and this
        is set to True, then we convert the image to grayscale (single-channel)
        image. If the source image has only one channel and this is set to False, then we retain the single channel input. Default: `False`.
        intensity_aug: Intensity augmentation configuration. Can be:
            - String: One of ['uniform_noise', 'gaussian_noise', 'contrast', 'brightness']
            - List of strings: Multiple intensity augmentations from the allowed values
            - Dictionary: Custom intensity configuration
            - None: No intensity augmentation applied
        geometric_aug: Geometric augmentation configuration. Can be:
            - String: One of ['rotation', 'scale', 'translate', 'erase_scale', 'mixup']
            - List of strings: Multiple geometric augmentations from the allowed values
            - Dictionary: Custom geometric configuration
            - None: No geometric augmentation applied
        scale: Factor to resize the image dimensions by, specified as a float. Default: 1.0.
        apply_aug: `True` if augmentations should be applied to the data pipeline,
            else `False`. Default: `False`.
        max_hw: Maximum height and width of images across the labels file. If `max_height` and
           `max_width` in the config is None, then `max_hw` is used (computed with
            `sleap_nn.data.providers.get_max_height_width`). Else the values in the config
            are used.
        cache_img: String to indicate which caching to use: `memory` or `disk`. If `None`,
            the images aren't cached and loaded from the `.slp` file on each access.
        cache_img_path: Path to save the `.jpg` files. If `None`, current working dir is used.
        use_existing_imgs: Use existing imgs/ chunks in the `cache_img_path`.
        confmap_head_config: DictConfig object with all the keys in the `head_config` section.
        (required keys: `sigma`, `output_stride` and `anchor_part` depending on the model type ).
        rank: Indicates the rank of the process. Used during distributed training to ensure that image storage to
            disk occurs only once across all workers.
    """

    def __init__(
        self,
        labels: List[sio.Labels],
        confmap_head_config: DictConfig,
        max_stride: int,
        user_instances_only: bool = True,
        ensure_rgb: bool = False,
        ensure_grayscale: bool = False,
        intensity_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
        geometric_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
        scale: float = 1.0,
        apply_aug: bool = False,
        max_hw: Tuple[Optional[int]] = (None, None),
        cache_img: Optional[str] = None,
        cache_img_path: Optional[str] = None,
        use_existing_imgs: bool = False,
        rank: Optional[int] = None,
    ) -> None:
        """Initialize class attributes."""
        super().__init__(
            labels=labels,
            max_stride=max_stride,
            user_instances_only=user_instances_only,
            ensure_rgb=ensure_rgb,
            ensure_grayscale=ensure_grayscale,
            intensity_aug=intensity_aug,
            geometric_aug=geometric_aug,
            scale=scale,
            apply_aug=apply_aug,
            max_hw=max_hw,
            cache_img=cache_img,
            cache_img_path=cache_img_path,
            use_existing_imgs=use_existing_imgs,
            rank=rank,
        )
        self.confmap_head_config = confmap_head_config

    def __getitem__(self, index) -> Dict:
        """Return dict with image and confmaps for instance for given index."""
        (labels_idx, lf_idx) = self.lf_idx_list[index]

        lf = self.labels[labels_idx][lf_idx]

        # load the img
        if self.cache_img is not None:
            if self.cache_img == "disk":
                img = np.array(
                    Image.open(
                        f"{self.cache_img_path}/sample_{labels_idx}_{lf_idx}.jpg"
                    )
                )
            elif self.cache_img == "memory":
                img = self.cache[(labels_idx, lf_idx)].copy()

        else:  # load from slp file if not cached
            img = lf.image

        if img.ndim == 2:
            img = np.expand_dims(img, axis=2)

        video_idx = self._get_video_idx(lf, labels_idx)

        # get dict
        sample = process_lf(
            lf,
            video_idx=video_idx,
            max_instances=self.max_instances,
            user_instances_only=self.user_instances_only,
        )

        # apply normalization
        sample["image"] = apply_normalization(sample["image"])

        if self.ensure_rgb:
            sample["image"] = convert_to_rgb(sample["image"])
        elif self.ensure_grayscale:
            sample["image"] = convert_to_grayscale(sample["image"])

        # size matcher
        sample["image"], eff_scale = apply_sizematcher(
            sample["image"],
            max_height=self.max_hw[0],
            max_width=self.max_hw[1],
        )
        sample["instances"] = sample["instances"] * eff_scale
        sample["eff_scale"] = torch.tensor(eff_scale, dtype=torch.float32)

        # resize image
        sample["image"], sample["instances"] = apply_resizer(
            sample["image"],
            sample["instances"],
            scale=self.scale,
        )

        # Pad the image (if needed) according max stride
        sample["image"] = apply_pad_to_stride(
            sample["image"], max_stride=self.max_stride
        )

        # apply augmentation
        if self.apply_aug:
            if self.intensity_aug is not None:
                sample["image"], sample["instances"] = apply_intensity_augmentation(
                    sample["image"],
                    sample["instances"],
                    **self.intensity_aug,
                )

            if self.geometric_aug is not None:
                sample["image"], sample["instances"] = apply_geometric_augmentation(
                    sample["image"],
                    sample["instances"],
                    **self.geometric_aug,
                )

        img_hw = sample["image"].shape[-2:]

        # Generate confidence maps
        confidence_maps = generate_confmaps(
            sample["instances"],
            img_hw=img_hw,
            sigma=self.confmap_head_config.sigma,
            output_stride=self.confmap_head_config.output_stride,
        )

        sample["confidence_maps"] = confidence_maps
        sample["labels_idx"] = labels_idx

        return sample

__getitem__(index)

Return dict with image and confmaps for instance for given index.

Source code in sleap_nn/data/custom_datasets.py 
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def __getitem__(self, index) -> Dict:
    """Return dict with image and confmaps for instance for given index."""
    (labels_idx, lf_idx) = self.lf_idx_list[index]

    lf = self.labels[labels_idx][lf_idx]

    # load the img
    if self.cache_img is not None:
        if self.cache_img == "disk":
            img = np.array(
                Image.open(
                    f"{self.cache_img_path}/sample_{labels_idx}_{lf_idx}.jpg"
                )
            )
        elif self.cache_img == "memory":
            img = self.cache[(labels_idx, lf_idx)].copy()

    else:  # load from slp file if not cached
        img = lf.image

    if img.ndim == 2:
        img = np.expand_dims(img, axis=2)

    video_idx = self._get_video_idx(lf, labels_idx)

    # get dict
    sample = process_lf(
        lf,
        video_idx=video_idx,
        max_instances=self.max_instances,
        user_instances_only=self.user_instances_only,
    )

    # apply normalization
    sample["image"] = apply_normalization(sample["image"])

    if self.ensure_rgb:
        sample["image"] = convert_to_rgb(sample["image"])
    elif self.ensure_grayscale:
        sample["image"] = convert_to_grayscale(sample["image"])

    # size matcher
    sample["image"], eff_scale = apply_sizematcher(
        sample["image"],
        max_height=self.max_hw[0],
        max_width=self.max_hw[1],
    )
    sample["instances"] = sample["instances"] * eff_scale
    sample["eff_scale"] = torch.tensor(eff_scale, dtype=torch.float32)

    # resize image
    sample["image"], sample["instances"] = apply_resizer(
        sample["image"],
        sample["instances"],
        scale=self.scale,
    )

    # Pad the image (if needed) according max stride
    sample["image"] = apply_pad_to_stride(
        sample["image"], max_stride=self.max_stride
    )

    # apply augmentation
    if self.apply_aug:
        if self.intensity_aug is not None:
            sample["image"], sample["instances"] = apply_intensity_augmentation(
                sample["image"],
                sample["instances"],
                **self.intensity_aug,
            )

        if self.geometric_aug is not None:
            sample["image"], sample["instances"] = apply_geometric_augmentation(
                sample["image"],
                sample["instances"],
                **self.geometric_aug,
            )

    img_hw = sample["image"].shape[-2:]

    # Generate confidence maps
    confidence_maps = generate_confmaps(
        sample["instances"],
        img_hw=img_hw,
        sigma=self.confmap_head_config.sigma,
        output_stride=self.confmap_head_config.output_stride,
    )

    sample["confidence_maps"] = confidence_maps
    sample["labels_idx"] = labels_idx

    return sample

__init__(labels, confmap_head_config, max_stride, user_instances_only=True, ensure_rgb=False, ensure_grayscale=False, intensity_aug=None, geometric_aug=None, scale=1.0, apply_aug=False, max_hw=(None, None), cache_img=None, cache_img_path=None, use_existing_imgs=False, rank=None)

Initialize class attributes.

Source code in sleap_nn/data/custom_datasets.py 
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def __init__(
    self,
    labels: List[sio.Labels],
    confmap_head_config: DictConfig,
    max_stride: int,
    user_instances_only: bool = True,
    ensure_rgb: bool = False,
    ensure_grayscale: bool = False,
    intensity_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
    geometric_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
    scale: float = 1.0,
    apply_aug: bool = False,
    max_hw: Tuple[Optional[int]] = (None, None),
    cache_img: Optional[str] = None,
    cache_img_path: Optional[str] = None,
    use_existing_imgs: bool = False,
    rank: Optional[int] = None,
) -> None:
    """Initialize class attributes."""
    super().__init__(
        labels=labels,
        max_stride=max_stride,
        user_instances_only=user_instances_only,
        ensure_rgb=ensure_rgb,
        ensure_grayscale=ensure_grayscale,
        intensity_aug=intensity_aug,
        geometric_aug=geometric_aug,
        scale=scale,
        apply_aug=apply_aug,
        max_hw=max_hw,
        cache_img=cache_img,
        cache_img_path=cache_img_path,
        use_existing_imgs=use_existing_imgs,
        rank=rank,
    )
    self.confmap_head_config = confmap_head_config

TopDownCenteredInstanceMultiClassDataset

Bases: CenteredInstanceDataset

Dataset class for instance-centered confidence map ID models.

Attributes:

Name Type Description
labels

Source sio.Labels object.
max_stride

Scalar integer specifying the maximum stride that the image must be divisible by.
anchor_ind

Index of the node to use as the anchor point, based on its index in the ordered list of skeleton nodes.
user_instances_only

True if only user labeled instances should be used for training. If False, both user labeled and predicted instances would be used.
ensure_rgb

(bool) True if the input image should have 3 channels (RGB image). If input has only one
is replicated along the channel axis. If the image has three channels and this is set to False, then we retain the three channels. Default

False.
ensure_grayscale

(bool) True if the input image should only have a single channel. If input has three channels (RGB) and this
image. If the source image has only one channel and this is set to False, then we retain the single channel input. Default

False.
intensity_aug

Intensity augmentation configuration. Can be: - String: One of ['uniform_noise', 'gaussian_noise', 'contrast', 'brightness'] - List of strings: Multiple intensity augmentations from the allowed values - Dictionary: Custom intensity configuration - None: No intensity augmentation applied
geometric_aug

Geometric augmentation configuration. Can be: - String: One of ['rotation', 'scale', 'translate', 'erase_scale', 'mixup'] - List of strings: Multiple geometric augmentations from the allowed values - Dictionary: Custom geometric configuration - None: No geometric augmentation applied
scale

Factor to resize the image dimensions by, specified as a float. Default: 1.0.
apply_aug

True if augmentations should be applied to the data pipeline, else False. Default: False.
max_hw

Maximum height and width of images across the labels file. If max_height and max_width in the config is None, then max_hw is used (computed with sleap_nn.data.providers.get_max_height_width). Else the values in the config are used.
cache_img

String to indicate which caching to use: memory or disk. If None, the images aren't cached and loaded from the .slp file on each access.
cache_img_path

Path to save the .jpg files. If None, current working dir is used.
use_existing_imgs

Use existing imgs/ chunks in the cache_img_path.
crop_size

Crop size of each instance for centered-instance model.
rank

Indicates the rank of the process. Used during distributed training to ensure that image storage to disk occurs only once across all workers.

Note: If scale is provided for centered-instance model, the images are cropped out from the scaled image with the given crop size.

Methods:

Name Description
__getitem__

Return dict with cropped image and confmaps of instance for given index.
__init__

Initialize class attributes.
Source code in sleap_nn/data/custom_datasets.py 
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class TopDownCenteredInstanceMultiClassDataset(CenteredInstanceDataset):
    """Dataset class for instance-centered confidence map ID models.

    Attributes:
        labels: Source `sio.Labels` object.
        max_stride: Scalar integer specifying the maximum stride that the image must be
            divisible by.
        anchor_ind: Index of the node to use as the anchor point, based on its index in the
            ordered list of skeleton nodes.
        user_instances_only: `True` if only user labeled instances should be used for training. If `False`,
            both user labeled and predicted instances would be used.
        ensure_rgb: (bool) True if the input image should have 3 channels (RGB image). If input has only one
        channel when this is set to `True`, then the images from single-channel
        is replicated along the channel axis. If the image has three channels and this is set to False, then we retain the three channels. Default: `False`.
        ensure_grayscale: (bool) True if the input image should only have a single channel. If input has three channels (RGB) and this
        is set to True, then we convert the image to grayscale (single-channel)
        image. If the source image has only one channel and this is set to False, then we retain the single channel input. Default: `False`.
        intensity_aug: Intensity augmentation configuration. Can be:
            - String: One of ['uniform_noise', 'gaussian_noise', 'contrast', 'brightness']
            - List of strings: Multiple intensity augmentations from the allowed values
            - Dictionary: Custom intensity configuration
            - None: No intensity augmentation applied
        geometric_aug: Geometric augmentation configuration. Can be:
            - String: One of ['rotation', 'scale', 'translate', 'erase_scale', 'mixup']
            - List of strings: Multiple geometric augmentations from the allowed values
            - Dictionary: Custom geometric configuration
            - None: No geometric augmentation applied
        scale: Factor to resize the image dimensions by, specified as a float. Default: 1.0.
        apply_aug: `True` if augmentations should be applied to the data pipeline,
            else `False`. Default: `False`.
        max_hw: Maximum height and width of images across the labels file. If `max_height` and
           `max_width` in the config is None, then `max_hw` is used (computed with
            `sleap_nn.data.providers.get_max_height_width`). Else the values in the config
            are used.
        cache_img: String to indicate which caching to use: `memory` or `disk`. If `None`,
            the images aren't cached and loaded from the `.slp` file on each access.
        cache_img_path: Path to save the `.jpg` files. If `None`, current working dir is used.
        use_existing_imgs: Use existing imgs/ chunks in the `cache_img_path`.
        crop_size: Crop size of each instance for centered-instance model.
        rank: Indicates the rank of the process. Used during distributed training to ensure that image storage to
            disk occurs only once across all workers.

    Note: If scale is provided for centered-instance model, the images are cropped out
    from the scaled image with the given crop size.
    """

    def __init__(
        self,
        labels: List[sio.Labels],
        crop_size: int,
        confmap_head_config: DictConfig,
        max_stride: int,
        anchor_ind: Optional[int] = None,
        user_instances_only: bool = True,
        ensure_rgb: bool = False,
        ensure_grayscale: bool = False,
        intensity_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
        geometric_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
        scale: float = 1.0,
        apply_aug: bool = False,
        max_hw: Tuple[Optional[int]] = (None, None),
        cache_img: Optional[str] = None,
        cache_img_path: Optional[str] = None,
        use_existing_imgs: bool = False,
        rank: Optional[int] = None,
    ) -> None:
        """Initialize class attributes."""
        super().__init__(
            labels=labels,
            crop_size=crop_size,
            confmap_head_config=confmap_head_config,
            max_stride=max_stride,
            anchor_ind=anchor_ind,
            user_instances_only=user_instances_only,
            ensure_rgb=ensure_rgb,
            ensure_grayscale=ensure_grayscale,
            intensity_aug=intensity_aug,
            geometric_aug=geometric_aug,
            scale=scale,
            apply_aug=apply_aug,
            max_hw=max_hw,
            cache_img=cache_img,
            cache_img_path=cache_img_path,
            use_existing_imgs=use_existing_imgs,
            rank=rank,
        )
        self.tracks = []
        for train_label in self.labels:
            self.tracks.extend([x.name for x in train_label.tracks if x is not None])
        self.tracks = list(set(self.tracks))

    def __getitem__(self, index) -> Dict:
        """Return dict with cropped image and confmaps of instance for given index."""
        labels_idx, lf_idx, inst_idx = self.instance_idx_list[index]
        lf = self.labels[labels_idx][lf_idx]

        if lf_idx == self.cache_lf[0]:
            img = self.cache_lf[1]
        else:
            # load the img
            if self.cache_img is not None:
                if self.cache_img == "disk":
                    img = np.array(
                        Image.open(
                            f"{self.cache_img_path}/sample_{labels_idx}_{lf_idx}.jpg"
                        )
                    )
                elif self.cache_img == "memory":
                    img = self.cache[(labels_idx, lf_idx)].copy()

            else:  # load from slp file if not cached
                img = lf.image  # TODO: doesn't work when num_workers > 0

            if img.ndim == 2:
                img = np.expand_dims(img, axis=2)

            self.cache_lf = [lf_idx, img]

        video_idx = self._get_video_idx(lf, labels_idx)

        image = np.transpose(img, (2, 0, 1))  # HWC -> CHW

        instances = []
        for inst in lf:
            instances.append(inst.numpy())
        instances = np.stack(instances, axis=0)

        # Add singleton time dimension for single frames.
        image = np.expand_dims(image, axis=0)  # (n_samples=1, C, H, W)
        instances = np.expand_dims(
            instances, axis=0
        )  # (n_samples=1, num_instances, num_nodes, 2)

        instances = torch.from_numpy(instances.astype("float32"))
        image = torch.from_numpy(image.copy())

        num_instances, _ = instances.shape[1:3]
        orig_img_height, orig_img_width = image.shape[-2:]

        instances = instances[:, inst_idx]

        # apply normalization
        image = apply_normalization(image)

        if self.ensure_rgb:
            image = convert_to_rgb(image)
        elif self.ensure_grayscale:
            image = convert_to_grayscale(image)

        # size matcher
        image, eff_scale = apply_sizematcher(
            image,
            max_height=self.max_hw[0],
            max_width=self.max_hw[1],
        )
        instances = instances * eff_scale

        # resize image
        image, instances = apply_resizer(
            image,
            instances,
            scale=self.scale,
        )

        # get class vectors
        track_ids = torch.Tensor(
            [
                (
                    self.tracks.index(lf.instances[idx].track.name)
                    if lf.instances[idx].track is not None
                    else -1
                )
                for idx in range(num_instances)
            ]
        ).to(torch.int32)
        class_vectors = make_class_vectors(
            class_inds=track_ids,
            n_classes=torch.tensor(len(self.tracks), dtype=torch.int32),
        )

        # get the centroids based on the anchor idx
        centroids = generate_centroids(instances, anchor_ind=self.anchor_ind)

        instance, centroid = instances[0], centroids[0]  # (n_samples=1)

        crop_size = np.array([self.crop_size, self.crop_size]) * np.sqrt(
            2
        )  # crop extra for rotation augmentation
        crop_size = crop_size.astype(np.int32).tolist()

        sample = generate_crops(image, instance, centroid, crop_size)

        sample["frame_idx"] = torch.tensor(lf.frame_idx, dtype=torch.int32)
        sample["video_idx"] = torch.tensor(video_idx, dtype=torch.int32)
        sample["num_instances"] = num_instances
        sample["orig_size"] = torch.Tensor([orig_img_height, orig_img_width])
        sample["eff_scale"] = torch.tensor(eff_scale, dtype=torch.float32)

        # apply augmentation
        if self.apply_aug:
            if self.intensity_aug is not None:
                (
                    sample["instance_image"],
                    sample["instance"],
                ) = apply_intensity_augmentation(
                    sample["instance_image"],
                    sample["instance"],
                    **self.intensity_aug,
                )

            if self.geometric_aug is not None:
                (
                    sample["instance_image"],
                    sample["instance"],
                ) = apply_geometric_augmentation(
                    sample["instance_image"],
                    sample["instance"],
                    **self.geometric_aug,
                )

        # re-crop to original crop size
        sample["instance_bbox"] = torch.unsqueeze(
            make_centered_bboxes(sample["centroid"][0], self.crop_size, self.crop_size),
            0,
        )  # (n_samples=1, 4, 2)

        sample["instance_image"] = crop_and_resize(
            sample["instance_image"],
            boxes=sample["instance_bbox"],
            size=(self.crop_size, self.crop_size),
        )
        point = sample["instance_bbox"][0][0]
        center_instance = sample["instance"] - point
        centered_centroid = sample["centroid"] - point

        sample["instance"] = center_instance  # (n_samples=1, n_nodes, 2)
        sample["centroid"] = centered_centroid  # (n_samples=1, 2)

        # Pad the image (if needed) according max stride
        sample["instance_image"] = apply_pad_to_stride(
            sample["instance_image"], max_stride=self.max_stride
        )

        img_hw = sample["instance_image"].shape[-2:]

        # Generate confidence maps
        confidence_maps = generate_confmaps(
            sample["instance"],
            img_hw=img_hw,
            sigma=self.confmap_head_config.sigma,
            output_stride=self.confmap_head_config.output_stride,
        )

        sample["track_id"] = track_ids[inst_idx]
        sample["class_vectors"] = class_vectors[inst_idx].to(torch.float32)

        sample["confidence_maps"] = confidence_maps
        sample["labels_idx"] = labels_idx

        return sample

__getitem__(index)

Return dict with cropped image and confmaps of instance for given index.

Source code in sleap_nn/data/custom_datasets.py 
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def __getitem__(self, index) -> Dict:
    """Return dict with cropped image and confmaps of instance for given index."""
    labels_idx, lf_idx, inst_idx = self.instance_idx_list[index]
    lf = self.labels[labels_idx][lf_idx]

    if lf_idx == self.cache_lf[0]:
        img = self.cache_lf[1]
    else:
        # load the img
        if self.cache_img is not None:
            if self.cache_img == "disk":
                img = np.array(
                    Image.open(
                        f"{self.cache_img_path}/sample_{labels_idx}_{lf_idx}.jpg"
                    )
                )
            elif self.cache_img == "memory":
                img = self.cache[(labels_idx, lf_idx)].copy()

        else:  # load from slp file if not cached
            img = lf.image  # TODO: doesn't work when num_workers > 0

        if img.ndim == 2:
            img = np.expand_dims(img, axis=2)

        self.cache_lf = [lf_idx, img]

    video_idx = self._get_video_idx(lf, labels_idx)

    image = np.transpose(img, (2, 0, 1))  # HWC -> CHW

    instances = []
    for inst in lf:
        instances.append(inst.numpy())
    instances = np.stack(instances, axis=0)

    # Add singleton time dimension for single frames.
    image = np.expand_dims(image, axis=0)  # (n_samples=1, C, H, W)
    instances = np.expand_dims(
        instances, axis=0
    )  # (n_samples=1, num_instances, num_nodes, 2)

    instances = torch.from_numpy(instances.astype("float32"))
    image = torch.from_numpy(image.copy())

    num_instances, _ = instances.shape[1:3]
    orig_img_height, orig_img_width = image.shape[-2:]

    instances = instances[:, inst_idx]

    # apply normalization
    image = apply_normalization(image)

    if self.ensure_rgb:
        image = convert_to_rgb(image)
    elif self.ensure_grayscale:
        image = convert_to_grayscale(image)

    # size matcher
    image, eff_scale = apply_sizematcher(
        image,
        max_height=self.max_hw[0],
        max_width=self.max_hw[1],
    )
    instances = instances * eff_scale

    # resize image
    image, instances = apply_resizer(
        image,
        instances,
        scale=self.scale,
    )

    # get class vectors
    track_ids = torch.Tensor(
        [
            (
                self.tracks.index(lf.instances[idx].track.name)
                if lf.instances[idx].track is not None
                else -1
            )
            for idx in range(num_instances)
        ]
    ).to(torch.int32)
    class_vectors = make_class_vectors(
        class_inds=track_ids,
        n_classes=torch.tensor(len(self.tracks), dtype=torch.int32),
    )

    # get the centroids based on the anchor idx
    centroids = generate_centroids(instances, anchor_ind=self.anchor_ind)

    instance, centroid = instances[0], centroids[0]  # (n_samples=1)

    crop_size = np.array([self.crop_size, self.crop_size]) * np.sqrt(
        2
    )  # crop extra for rotation augmentation
    crop_size = crop_size.astype(np.int32).tolist()

    sample = generate_crops(image, instance, centroid, crop_size)

    sample["frame_idx"] = torch.tensor(lf.frame_idx, dtype=torch.int32)
    sample["video_idx"] = torch.tensor(video_idx, dtype=torch.int32)
    sample["num_instances"] = num_instances
    sample["orig_size"] = torch.Tensor([orig_img_height, orig_img_width])
    sample["eff_scale"] = torch.tensor(eff_scale, dtype=torch.float32)

    # apply augmentation
    if self.apply_aug:
        if self.intensity_aug is not None:
            (
                sample["instance_image"],
                sample["instance"],
            ) = apply_intensity_augmentation(
                sample["instance_image"],
                sample["instance"],
                **self.intensity_aug,
            )

        if self.geometric_aug is not None:
            (
                sample["instance_image"],
                sample["instance"],
            ) = apply_geometric_augmentation(
                sample["instance_image"],
                sample["instance"],
                **self.geometric_aug,
            )

    # re-crop to original crop size
    sample["instance_bbox"] = torch.unsqueeze(
        make_centered_bboxes(sample["centroid"][0], self.crop_size, self.crop_size),
        0,
    )  # (n_samples=1, 4, 2)

    sample["instance_image"] = crop_and_resize(
        sample["instance_image"],
        boxes=sample["instance_bbox"],
        size=(self.crop_size, self.crop_size),
    )
    point = sample["instance_bbox"][0][0]
    center_instance = sample["instance"] - point
    centered_centroid = sample["centroid"] - point

    sample["instance"] = center_instance  # (n_samples=1, n_nodes, 2)
    sample["centroid"] = centered_centroid  # (n_samples=1, 2)

    # Pad the image (if needed) according max stride
    sample["instance_image"] = apply_pad_to_stride(
        sample["instance_image"], max_stride=self.max_stride
    )

    img_hw = sample["instance_image"].shape[-2:]

    # Generate confidence maps
    confidence_maps = generate_confmaps(
        sample["instance"],
        img_hw=img_hw,
        sigma=self.confmap_head_config.sigma,
        output_stride=self.confmap_head_config.output_stride,
    )

    sample["track_id"] = track_ids[inst_idx]
    sample["class_vectors"] = class_vectors[inst_idx].to(torch.float32)

    sample["confidence_maps"] = confidence_maps
    sample["labels_idx"] = labels_idx

    return sample

__init__(labels, crop_size, confmap_head_config, max_stride, anchor_ind=None, user_instances_only=True, ensure_rgb=False, ensure_grayscale=False, intensity_aug=None, geometric_aug=None, scale=1.0, apply_aug=False, max_hw=(None, None), cache_img=None, cache_img_path=None, use_existing_imgs=False, rank=None)

Initialize class attributes.

Source code in sleap_nn/data/custom_datasets.py 
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def __init__(
    self,
    labels: List[sio.Labels],
    crop_size: int,
    confmap_head_config: DictConfig,
    max_stride: int,
    anchor_ind: Optional[int] = None,
    user_instances_only: bool = True,
    ensure_rgb: bool = False,
    ensure_grayscale: bool = False,
    intensity_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
    geometric_aug: Optional[Union[str, List[str], Dict[str, Any]]] = None,
    scale: float = 1.0,
    apply_aug: bool = False,
    max_hw: Tuple[Optional[int]] = (None, None),
    cache_img: Optional[str] = None,
    cache_img_path: Optional[str] = None,
    use_existing_imgs: bool = False,
    rank: Optional[int] = None,
) -> None:
    """Initialize class attributes."""
    super().__init__(
        labels=labels,
        crop_size=crop_size,
        confmap_head_config=confmap_head_config,
        max_stride=max_stride,
        anchor_ind=anchor_ind,
        user_instances_only=user_instances_only,
        ensure_rgb=ensure_rgb,
        ensure_grayscale=ensure_grayscale,
        intensity_aug=intensity_aug,
        geometric_aug=geometric_aug,
        scale=scale,
        apply_aug=apply_aug,
        max_hw=max_hw,
        cache_img=cache_img,
        cache_img_path=cache_img_path,
        use_existing_imgs=use_existing_imgs,
        rank=rank,
    )
    self.tracks = []
    for train_label in self.labels:
        self.tracks.extend([x.name for x in train_label.tracks if x is not None])
    self.tracks = list(set(self.tracks))

get_steps_per_epoch(dataset, batch_size)

Compute the number of steps (iterations) per epoch for the given dataset.

Source code in sleap_nn/data/custom_datasets.py 
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def get_steps_per_epoch(dataset: BaseDataset, batch_size: int):
    """Compute the number of steps (iterations) per epoch for the given dataset."""
    return (len(dataset) // batch_size) + (1 if (len(dataset) % batch_size) else 0)

get_train_val_dataloaders(train_dataset, val_dataset, config, train_steps_per_epoch=None, val_steps_per_epoch=None, rank=None, trainer_devices=1)

Return the train and val dataloaders.

Parameters:

Name Type Description Default
train_dataset BaseDataset

Train dataset-instance of one of the dataset classes [SingleInstanceDataset, CentroidDataset, CenteredInstanceDataset, BottomUpDataset, BottomUpMultiClassDataset, TopDownCenteredInstanceMultiClassDataset].

 required
val_dataset BaseDataset

Val dataset-instance of one of the dataset classes [SingleInstanceDataset, CentroidDataset, CenteredInstanceDataset, BottomUpDataset, BottomUpMultiClassDataset, TopDownCenteredInstanceMultiClassDataset].

 required
config DictConfig

Sleap-nn config.

 required
train_steps_per_epoch Optional[int]

Number of minibatches (steps) to train for in an epoch. If set to None, this is set to the number of batches in the training data. Note: In a multi-gpu training setup, the effective steps during training would be the trainer_steps_per_epoch / trainer_devices.

 None
val_steps_per_epoch Optional[int]

Number of minibatches (steps) to run validation for in an epoch. If set to None, this is set to the number of batches in the val data.

 None
rank Optional[int]

Indicates the rank of the process. Used during distributed training to ensure that image storage to disk occurs only once across all workers.

 None
trainer_devices int

Number of devices to use for training.

 1

Returns:

Type Description

A tuple (train_dataloader, val_dataloader).
Source code in sleap_nn/data/custom_datasets.py 
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def get_train_val_dataloaders(
    train_dataset: BaseDataset,
    val_dataset: BaseDataset,
    config: DictConfig,
    train_steps_per_epoch: Optional[int] = None,
    val_steps_per_epoch: Optional[int] = None,
    rank: Optional[int] = None,
    trainer_devices: int = 1,
):
    """Return the train and val dataloaders.

    Args:
        train_dataset: Train dataset-instance of one of the dataset classes [SingleInstanceDataset, CentroidDataset, CenteredInstanceDataset, BottomUpDataset, BottomUpMultiClassDataset, TopDownCenteredInstanceMultiClassDataset].
        val_dataset: Val dataset-instance of one of the dataset classes [SingleInstanceDataset, CentroidDataset, CenteredInstanceDataset, BottomUpDataset, BottomUpMultiClassDataset, TopDownCenteredInstanceMultiClassDataset].
        config: Sleap-nn config.
        train_steps_per_epoch: Number of minibatches (steps) to train for in an epoch. If set to `None`, this is set to the number of batches in the training data. **Note**: In a multi-gpu training setup, the effective steps during training would be the `trainer_steps_per_epoch` / `trainer_devices`.
        val_steps_per_epoch: Number of minibatches (steps) to run validation for in an epoch. If set to `None`, this is set to the number of batches in the val data.
        rank: Indicates the rank of the process. Used during distributed training to ensure that image storage to
            disk occurs only once across all workers.
        trainer_devices: Number of devices to use for training.

    Returns:
        A tuple (train_dataloader, val_dataloader).
    """
    pin_memory = (
        config.trainer_config.train_data_loader.pin_memory
        if "pin_memory" in config.trainer_config.train_data_loader
        and config.trainer_config.train_data_loader.pin_memory is not None
        else True
    )

    if train_steps_per_epoch is None:
        train_steps_per_epoch = config.trainer_config.train_steps_per_epoch
        if train_steps_per_epoch is None:
            train_steps_per_epoch = get_steps_per_epoch(
                dataset=train_dataset,
                batch_size=config.trainer_config.train_data_loader.batch_size,
            )

    if val_steps_per_epoch is None:
        val_steps_per_epoch = get_steps_per_epoch(
            dataset=val_dataset,
            batch_size=config.trainer_config.val_data_loader.batch_size,
        )

    train_sampler = (
        DistributedSampler(
            dataset=train_dataset,
            shuffle=config.trainer_config.train_data_loader.shuffle,
            rank=rank if rank is not None else 0,
            num_replicas=trainer_devices,
        )
        if trainer_devices > 1
        else None
    )

    train_data_loader = InfiniteDataLoader(
        dataset=train_dataset,
        sampler=train_sampler,
        len_dataloader=max(1, round(train_steps_per_epoch / trainer_devices)),
        shuffle=(
            config.trainer_config.train_data_loader.shuffle
            if train_sampler is None
            else None
        ),
        batch_size=config.trainer_config.train_data_loader.batch_size,
        num_workers=config.trainer_config.train_data_loader.num_workers,
        pin_memory=pin_memory,
        persistent_workers=(
            True if config.trainer_config.train_data_loader.num_workers > 0 else None
        ),
        prefetch_factor=(
            config.trainer_config.train_data_loader.batch_size
            if config.trainer_config.train_data_loader.num_workers > 0
            else None
        ),
    )

    val_sampler = (
        DistributedSampler(
            dataset=val_dataset,
            shuffle=False,
            rank=rank if rank is not None else 0,
            num_replicas=trainer_devices,
        )
        if trainer_devices > 1
        else None
    )
    val_data_loader = InfiniteDataLoader(
        dataset=val_dataset,
        shuffle=False if val_sampler is None else None,
        sampler=val_sampler,
        len_dataloader=(
            max(1, round(val_steps_per_epoch / trainer_devices))
            if trainer_devices > 1
            else None
        ),
        batch_size=config.trainer_config.val_data_loader.batch_size,
        num_workers=config.trainer_config.val_data_loader.num_workers,
        pin_memory=pin_memory,
        persistent_workers=(
            True if config.trainer_config.val_data_loader.num_workers > 0 else None
        ),
        prefetch_factor=(
            config.trainer_config.val_data_loader.batch_size
            if config.trainer_config.val_data_loader.num_workers > 0
            else None
        ),
    )

    return train_data_loader, val_data_loader

get_train_val_datasets(train_labels, val_labels, config, rank=None)

Return the train and val datasets.

Parameters:

Name Type Description Default
train_labels List[Labels]

List of train labels.

 required
val_labels List[Labels]

List of val labels.

 required
config DictConfig

Sleap-nn config.

 required
rank Optional[int]

Indicates the rank of the process. Used during distributed training to ensure that image storage to disk occurs only once across all workers.

 None

Returns:

Type Description

A tuple (train_dataset, val_dataset).
Source code in sleap_nn/data/custom_datasets.py 
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def get_train_val_datasets(
    train_labels: List[sio.Labels],
    val_labels: List[sio.Labels],
    config: DictConfig,
    rank: Optional[int] = None,
):
    """Return the train and val datasets.

    Args:
        train_labels: List of train labels.
        val_labels: List of val labels.
        config: Sleap-nn config.
        rank: Indicates the rank of the process. Used during distributed training to ensure that image storage to
            disk occurs only once across all workers.

    Returns:
        A tuple (train_dataset, val_dataset).
    """
    cache_imgs = (
        config.data_config.data_pipeline_fw.split("_")[-1]
        if "cache_img" in config.data_config.data_pipeline_fw
        else None
    )
    base_cache_img_path = config.data_config.cache_img_path
    train_cache_img_path, val_cache_img_path = None, None

    if cache_imgs == "disk":
        train_cache_img_path = Path(base_cache_img_path) / "train_imgs"
        val_cache_img_path = Path(base_cache_img_path) / "val_imgs"
    use_existing_imgs = config.data_config.use_existing_imgs

    model_type = get_model_type_from_cfg(config=config)
    backbone_type = get_backbone_type_from_cfg(config=config)

    if cache_imgs == "disk" and use_existing_imgs:
        if not (
            train_cache_img_path.exists()
            and train_cache_img_path.is_dir()
            and any(train_cache_img_path.glob("*.jpg"))
        ):
            message = f"There are no images in the path: {train_cache_img_path}"
            logger.error(message)
            raise Exception(message)

        if not (
            val_cache_img_path.exists()
            and val_cache_img_path.is_dir()
            and any(val_cache_img_path.glob("*.jpg"))
        ):
            message = f"There are no images in the path: {val_cache_img_path}"
            logger.error(message)
            raise Exception(message)

    if model_type == "bottomup":
        train_dataset = BottomUpDataset(
            labels=train_labels,
            confmap_head_config=config.model_config.head_configs.bottomup.confmaps,
            pafs_head_config=config.model_config.head_configs.bottomup.pafs,
            max_stride=config.model_config.backbone_config[f"{backbone_type}"][
                "max_stride"
            ],
            user_instances_only=config.data_config.user_instances_only,
            ensure_rgb=config.data_config.preprocessing.ensure_rgb,
            ensure_grayscale=config.data_config.preprocessing.ensure_grayscale,
            intensity_aug=(
                config.data_config.augmentation_config.intensity
                if config.data_config.augmentation_config is not None
                else None
            ),
            geometric_aug=(
                config.data_config.augmentation_config.geometric
                if config.data_config.augmentation_config is not None
                else None
            ),
            scale=config.data_config.preprocessing.scale,
            apply_aug=config.data_config.use_augmentations_train,
            max_hw=(
                config.data_config.preprocessing.max_height,
                config.data_config.preprocessing.max_width,
            ),
            cache_img=cache_imgs,
            cache_img_path=train_cache_img_path,
            use_existing_imgs=use_existing_imgs,
            rank=rank,
        )
        val_dataset = BottomUpDataset(
            labels=val_labels,
            confmap_head_config=config.model_config.head_configs.bottomup.confmaps,
            pafs_head_config=config.model_config.head_configs.bottomup.pafs,
            max_stride=config.model_config.backbone_config[f"{backbone_type}"][
                "max_stride"
            ],
            user_instances_only=config.data_config.user_instances_only,
            ensure_rgb=config.data_config.preprocessing.ensure_rgb,
            ensure_grayscale=config.data_config.preprocessing.ensure_grayscale,
            intensity_aug=None,
            geometric_aug=None,
            scale=config.data_config.preprocessing.scale,
            apply_aug=False,
            max_hw=(
                config.data_config.preprocessing.max_height,
                config.data_config.preprocessing.max_width,
            ),
            cache_img=cache_imgs,
            cache_img_path=val_cache_img_path,
            use_existing_imgs=use_existing_imgs,
            rank=rank,
        )

    elif model_type == "multi_class_bottomup":
        train_dataset = BottomUpMultiClassDataset(
            labels=train_labels,
            confmap_head_config=config.model_config.head_configs.multi_class_bottomup.confmaps,
            class_maps_head_config=config.model_config.head_configs.multi_class_bottomup.class_maps,
            max_stride=config.model_config.backbone_config[f"{backbone_type}"][
                "max_stride"
            ],
            user_instances_only=config.data_config.user_instances_only,
            ensure_rgb=config.data_config.preprocessing.ensure_rgb,
            ensure_grayscale=config.data_config.preprocessing.ensure_grayscale,
            intensity_aug=(
                config.data_config.augmentation_config.intensity
                if config.data_config.augmentation_config is not None
                else None
            ),
            geometric_aug=(
                config.data_config.augmentation_config.geometric
                if config.data_config.augmentation_config is not None
                else None
            ),
            scale=config.data_config.preprocessing.scale,
            apply_aug=config.data_config.use_augmentations_train,
            max_hw=(
                config.data_config.preprocessing.max_height,
                config.data_config.preprocessing.max_width,
            ),
            cache_img=cache_imgs,
            cache_img_path=train_cache_img_path,
            use_existing_imgs=use_existing_imgs,
            rank=rank,
        )
        val_dataset = BottomUpMultiClassDataset(
            labels=val_labels,
            confmap_head_config=config.model_config.head_configs.multi_class_bottomup.confmaps,
            class_maps_head_config=config.model_config.head_configs.multi_class_bottomup.class_maps,
            max_stride=config.model_config.backbone_config[f"{backbone_type}"][
                "max_stride"
            ],
            user_instances_only=config.data_config.user_instances_only,
            ensure_rgb=config.data_config.preprocessing.ensure_rgb,
            ensure_grayscale=config.data_config.preprocessing.ensure_grayscale,
            intensity_aug=None,
            geometric_aug=None,
            scale=config.data_config.preprocessing.scale,
            apply_aug=False,
            max_hw=(
                config.data_config.preprocessing.max_height,
                config.data_config.preprocessing.max_width,
            ),
            cache_img=cache_imgs,
            cache_img_path=val_cache_img_path,
            use_existing_imgs=use_existing_imgs,
            rank=rank,
        )

    elif model_type == "centered_instance":
        nodes = config.model_config.head_configs.centered_instance.confmaps.part_names
        anchor_part = (
            config.model_config.head_configs.centered_instance.confmaps.anchor_part
        )
        anchor_ind = nodes.index(anchor_part) if anchor_part is not None else None
        train_dataset = CenteredInstanceDataset(
            labels=train_labels,
            confmap_head_config=config.model_config.head_configs.centered_instance.confmaps,
            max_stride=config.model_config.backbone_config[f"{backbone_type}"][
                "max_stride"
            ],
            anchor_ind=anchor_ind,
            user_instances_only=config.data_config.user_instances_only,
            ensure_rgb=config.data_config.preprocessing.ensure_rgb,
            ensure_grayscale=config.data_config.preprocessing.ensure_grayscale,
            intensity_aug=(
                config.data_config.augmentation_config.intensity
                if config.data_config.augmentation_config is not None
                else None
            ),
            geometric_aug=(
                config.data_config.augmentation_config.geometric
                if config.data_config.augmentation_config is not None
                else None
            ),
            scale=config.data_config.preprocessing.scale,
            apply_aug=config.data_config.use_augmentations_train,
            crop_size=config.data_config.preprocessing.crop_size,
            max_hw=(
                config.data_config.preprocessing.max_height,
                config.data_config.preprocessing.max_width,
            ),
            cache_img=cache_imgs,
            cache_img_path=train_cache_img_path,
            use_existing_imgs=use_existing_imgs,
            rank=rank,
        )
        val_dataset = CenteredInstanceDataset(
            labels=val_labels,
            confmap_head_config=config.model_config.head_configs.centered_instance.confmaps,
            max_stride=config.model_config.backbone_config[f"{backbone_type}"][
                "max_stride"
            ],
            anchor_ind=anchor_ind,
            user_instances_only=config.data_config.user_instances_only,
            ensure_rgb=config.data_config.preprocessing.ensure_rgb,
            ensure_grayscale=config.data_config.preprocessing.ensure_grayscale,
            intensity_aug=None,
            geometric_aug=None,
            scale=config.data_config.preprocessing.scale,
            apply_aug=False,
            crop_size=config.data_config.preprocessing.crop_size,
            max_hw=(
                config.data_config.preprocessing.max_height,
                config.data_config.preprocessing.max_width,
            ),
            cache_img=cache_imgs,
            cache_img_path=val_cache_img_path,
            use_existing_imgs=use_existing_imgs,
            rank=rank,
        )

    elif model_type == "multi_class_topdown":
        nodes = config.model_config.head_configs.multi_class_topdown.confmaps.part_names
        anchor_part = (
            config.model_config.head_configs.multi_class_topdown.confmaps.anchor_part
        )
        anchor_ind = nodes.index(anchor_part) if anchor_part is not None else None
        train_dataset = TopDownCenteredInstanceMultiClassDataset(
            labels=train_labels,
            confmap_head_config=config.model_config.head_configs.multi_class_topdown.confmaps,
            max_stride=config.model_config.backbone_config[f"{backbone_type}"][
                "max_stride"
            ],
            anchor_ind=anchor_ind,
            user_instances_only=config.data_config.user_instances_only,
            ensure_rgb=config.data_config.preprocessing.ensure_rgb,
            ensure_grayscale=config.data_config.preprocessing.ensure_grayscale,
            intensity_aug=(
                config.data_config.augmentation_config.intensity
                if config.data_config.augmentation_config is not None
                else None
            ),
            geometric_aug=(
                config.data_config.augmentation_config.geometric
                if config.data_config.augmentation_config is not None
                else None
            ),
            scale=config.data_config.preprocessing.scale,
            apply_aug=config.data_config.use_augmentations_train,
            crop_size=config.data_config.preprocessing.crop_size,
            max_hw=(
                config.data_config.preprocessing.max_height,
                config.data_config.preprocessing.max_width,
            ),
            cache_img=cache_imgs,
            cache_img_path=train_cache_img_path,
            use_existing_imgs=use_existing_imgs,
            rank=rank,
        )
        val_dataset = TopDownCenteredInstanceMultiClassDataset(
            labels=val_labels,
            confmap_head_config=config.model_config.head_configs.multi_class_topdown.confmaps,
            max_stride=config.model_config.backbone_config[f"{backbone_type}"][
                "max_stride"
            ],
            anchor_ind=anchor_ind,
            user_instances_only=config.data_config.user_instances_only,
            ensure_rgb=config.data_config.preprocessing.ensure_rgb,
            ensure_grayscale=config.data_config.preprocessing.ensure_grayscale,
            intensity_aug=None,
            geometric_aug=None,
            scale=config.data_config.preprocessing.scale,
            apply_aug=False,
            crop_size=config.data_config.preprocessing.crop_size,
            max_hw=(
                config.data_config.preprocessing.max_height,
                config.data_config.preprocessing.max_width,
            ),
            cache_img=cache_imgs,
            cache_img_path=val_cache_img_path,
            use_existing_imgs=use_existing_imgs,
            rank=rank,
        )

    elif model_type == "centroid":
        nodes = [x["name"] for x in config.data_config.skeletons[0]["nodes"]]
        anchor_part = config.model_config.head_configs.centroid.confmaps.anchor_part
        anchor_ind = nodes.index(anchor_part) if anchor_part is not None else None
        train_dataset = CentroidDataset(
            labels=train_labels,
            confmap_head_config=config.model_config.head_configs.centroid.confmaps,
            max_stride=config.model_config.backbone_config[f"{backbone_type}"][
                "max_stride"
            ],
            anchor_ind=anchor_ind,
            user_instances_only=config.data_config.user_instances_only,
            ensure_rgb=config.data_config.preprocessing.ensure_rgb,
            ensure_grayscale=config.data_config.preprocessing.ensure_grayscale,
            intensity_aug=(
                config.data_config.augmentation_config.intensity
                if config.data_config.augmentation_config is not None
                else None
            ),
            geometric_aug=(
                config.data_config.augmentation_config.geometric
                if config.data_config.augmentation_config is not None
                else None
            ),
            scale=config.data_config.preprocessing.scale,
            apply_aug=config.data_config.use_augmentations_train,
            max_hw=(
                config.data_config.preprocessing.max_height,
                config.data_config.preprocessing.max_width,
            ),
            cache_img=cache_imgs,
            cache_img_path=train_cache_img_path,
            use_existing_imgs=use_existing_imgs,
            rank=rank,
        )
        val_dataset = CentroidDataset(
            labels=val_labels,
            confmap_head_config=config.model_config.head_configs.centroid.confmaps,
            max_stride=config.model_config.backbone_config[f"{backbone_type}"][
                "max_stride"
            ],
            anchor_ind=anchor_ind,
            user_instances_only=config.data_config.user_instances_only,
            ensure_rgb=config.data_config.preprocessing.ensure_rgb,
            ensure_grayscale=config.data_config.preprocessing.ensure_grayscale,
            intensity_aug=None,
            geometric_aug=None,
            scale=config.data_config.preprocessing.scale,
            apply_aug=False,
            max_hw=(
                config.data_config.preprocessing.max_height,
                config.data_config.preprocessing.max_width,
            ),
            cache_img=cache_imgs,
            cache_img_path=val_cache_img_path,
            use_existing_imgs=use_existing_imgs,
            rank=rank,
        )

    else:
        train_dataset = SingleInstanceDataset(
            labels=train_labels,
            confmap_head_config=config.model_config.head_configs.single_instance.confmaps,
            max_stride=config.model_config.backbone_config[f"{backbone_type}"][
                "max_stride"
            ],
            user_instances_only=config.data_config.user_instances_only,
            ensure_rgb=config.data_config.preprocessing.ensure_rgb,
            ensure_grayscale=config.data_config.preprocessing.ensure_grayscale,
            intensity_aug=(
                config.data_config.augmentation_config.intensity
                if config.data_config.augmentation_config is not None
                else None
            ),
            geometric_aug=(
                config.data_config.augmentation_config.geometric
                if config.data_config.augmentation_config is not None
                else None
            ),
            scale=config.data_config.preprocessing.scale,
            apply_aug=config.data_config.use_augmentations_train,
            max_hw=(
                config.data_config.preprocessing.max_height,
                config.data_config.preprocessing.max_width,
            ),
            cache_img=cache_imgs,
            cache_img_path=train_cache_img_path,
            use_existing_imgs=use_existing_imgs,
            rank=rank,
        )
        val_dataset = SingleInstanceDataset(
            labels=val_labels,
            confmap_head_config=config.model_config.head_configs.single_instance.confmaps,
            max_stride=config.model_config.backbone_config[f"{backbone_type}"][
                "max_stride"
            ],
            user_instances_only=config.data_config.user_instances_only,
            ensure_rgb=config.data_config.preprocessing.ensure_rgb,
            ensure_grayscale=config.data_config.preprocessing.ensure_grayscale,
            intensity_aug=None,
            geometric_aug=None,
            scale=config.data_config.preprocessing.scale,
            apply_aug=False,
            max_hw=(
                config.data_config.preprocessing.max_height,
                config.data_config.preprocessing.max_width,
            ),
            cache_img=cache_imgs,
            cache_img_path=val_cache_img_path,
            use_existing_imgs=use_existing_imgs,
            rank=rank,
        )

    # If using caching, close the videos to prevent `h5py objects can't be pickled error` when num_workers > 0.
    if "cache_img" in config.data_config.data_pipeline_fw:
        for train, val in zip(train_labels, val_labels):
            for video in train.videos:
                if video.is_open:
                    video.close()
            for video in val.videos:
                if video.is_open:
                    video.close()

    return train_dataset, val_dataset