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RemoveLabels

Bases: Transform

Set specified label values to a background value.

This is a convenience wrapper that builds a remapping dict internally. For more control, use RemapLabels directly.

Only LabelMap images are affected.

Parameters:

Name Type Description Default
labels Sequence[int]

Label values to remove.

 required
background_label int

Value to assign to removed labels.

 0
**kwargs Any

See Transform.

 {}

Examples:

>>> import torchio as tio
>>> transform = tio.RemoveLabels([3, 4, 5])
>>> transform = tio.RemoveLabels([2], background_label=0)
Source code in src/torchio/transforms/label/remove_labels.py 
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class RemoveLabels(Transform):
    """Set specified label values to a background value.

    This is a convenience wrapper that builds a remapping dict
    internally.  For more control, use
    [`RemapLabels`][torchio.RemapLabels] directly.

    Only [`LabelMap`][torchio.LabelMap] images are affected.

    Args:
        labels: Label values to remove.
        background_label: Value to assign to removed labels.
        **kwargs: See [`Transform`][torchio.Transform].

    Examples:
        >>> import torchio as tio
        >>> transform = tio.RemoveLabels([3, 4, 5])
        >>> transform = tio.RemoveLabels([2], background_label=0)
    """

    def __init__(
        self,
        labels: Sequence[int],
        *,
        background_label: int = 0,
        **kwargs: Any,
    ) -> None:
        super().__init__(**kwargs)
        self.labels = list(labels)
        self.background_label = background_label

    def make_params(self, batch: SubjectsBatch) -> dict[str, Any]:
        """No random parameters."""
        return {}

    def apply_transform(
        self,
        batch: SubjectsBatch,
        params: dict[str, Any],
    ) -> SubjectsBatch:
        """Set specified labels to the background value."""
        for _name, img_batch in batch.images.items():
            if not issubclass(img_batch._image_class, LabelMap):
                continue
            data = img_batch.data.clone()
            for label in self.labels:
                data[img_batch.data == label] = self.background_label
            img_batch.data = data
        return batch

invertible property

Whether this transform can be inverted.

forward(data) 

forward(data: Subject) -> Subject

forward(data: Image) -> Image

forward(data: Tensor) -> Tensor

forward(data: np.ndarray) -> np.ndarray

forward(data: sitk.Image) -> sitk.Image

forward(data: nib.Nifti1Image) -> nib.Nifti1Image

forward(data: dict) -> dict

forward(data: ImagesBatch) -> ImagesBatch

forward(data: SubjectsBatch) -> SubjectsBatch

Apply the transform.

The output type always matches the input type.

Parameters:

Name Type Description Default
data Any

Input data to transform.

 required
Source code in src/torchio/transforms/transform.py 
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def forward(self, data: Any) -> Any:
    """Apply the transform.

    The output type always matches the input type.

    Args:
        data: Input data to transform.
    """
    if self.copy:
        data = _copy.deepcopy(data)
    batch, unwrap = self._wrap(data)
    if torch.rand(1).item() > self.p:
        return unwrap(batch)
    params = self.make_params(batch)
    batch = self.apply_transform(batch, params)
    # Record history on the batch
    trace = AppliedTransform(name=type(self).__name__, params=params)
    if not hasattr(batch, "applied_transforms"):
        batch.applied_transforms = []
    batch.applied_transforms.append(trace)
    result = unwrap(batch)
    # Propagate history to outputs that can carry it
    if (
        hasattr(batch, "applied_transforms")
        and not isinstance(result, (SubjectsBatch, Tensor, np.ndarray))
        and not isinstance(result, dict)
    ):
        with contextlib.suppress(AttributeError):
            result.applied_transforms = list(batch.applied_transforms)
    return result

inverse(params)

Return a transform that undoes this one.

Override in invertible subclasses. The returned transform, when applied, reverses the effect of the forward pass with the given parameters.

Parameters:

Name Type Description Default
params dict[str, Any]

The parameters recorded in the forward pass.

 required

Returns:

Type Description
Transform

A new Transform instance that inverts this one.
Source code in src/torchio/transforms/transform.py 
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def inverse(self, params: dict[str, Any]) -> Transform:
    """Return a transform that undoes this one.

    Override in invertible subclasses. The returned transform,
    when applied, reverses the effect of the forward pass with
    the given parameters.

    Args:
        params: The parameters recorded in the forward pass.

    Returns:
        A new `Transform` instance that inverts this one.
    """
    msg = f"{type(self).__name__} is not invertible"
    raise NotImplementedError(msg)

to_hydra()

Export as a Hydra-compatible config dict.

Returns a dict with _target_ set to the fully qualified class name and only non-default field values included.

Returns:

Type Description
dict[str, Any]

Dict suitable for hydra.utils.instantiate().
Source code in src/torchio/transforms/transform.py 
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def to_hydra(self) -> dict[str, Any]:
    """Export as a Hydra-compatible config dict.

    Returns a dict with `_target_` set to the fully qualified
    class name and only non-default field values included.

    Returns:
        Dict suitable for `hydra.utils.instantiate()`.
    """
    from .parameter_range import _ParameterRange

    cls = type(self)
    target = f"torchio.{cls.__qualname__}"
    cfg: dict[str, Any] = {"_target_": target}

    for name, default in _collect_init_params(cls).items():
        value = getattr(self, name, default)
        if isinstance(value, _ParameterRange):
            if value._original == default:
                continue
            value = _hydra_value(value._original)
        elif value == default:
            continue
        else:
            value = _hydra_value(value)
        cfg[name] = value
    return cfg

make_params(batch)

No random parameters.

Source code in src/torchio/transforms/label/remove_labels.py 
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def make_params(self, batch: SubjectsBatch) -> dict[str, Any]:
    """No random parameters."""
    return {}

apply_transform(batch, params)

Set specified labels to the background value.

Source code in src/torchio/transforms/label/remove_labels.py 
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def apply_transform(
    self,
    batch: SubjectsBatch,
    params: dict[str, Any],
) -> SubjectsBatch:
    """Set specified labels to the background value."""
    for _name, img_batch in batch.images.items():
        if not issubclass(img_batch._image_class, LabelMap):
            continue
        data = img_batch.data.clone()
        for label in self.labels:
            data[img_batch.data == label] = self.background_label
        img_batch.data = data
    return batch