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RemapLabels

Bases: Transform

Reassign label values in label maps.

Each key in the remapping dict is replaced by its value. Labels not mentioned in the dict are left unchanged.

Only LabelMap images are affected.

Parameters:

Name Type Description Default
remapping dict[int, int]

Dictionary mapping old labels to new labels.

 required
**kwargs Any

See Transform.

 {}

Examples:

>>> import torchio as tio
>>> # Merge labels 2 and 3 into label 1
>>> transform = tio.RemapLabels({2: 1, 3: 1})
>>> # Swap labels 1 and 2
>>> transform = tio.RemapLabels({1: 2, 2: 1})
Source code in src/torchio/transforms/label/remap_labels.py 
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class RemapLabels(Transform):
    r"""Reassign label values in label maps.

    Each key in the *remapping* dict is replaced by its value.
    Labels not mentioned in the dict are left unchanged.

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

    Args:
        remapping: Dictionary mapping old labels to new labels.
        **kwargs: See [`Transform`][torchio.Transform].

    Examples:
        >>> import torchio as tio
        >>> # Merge labels 2 and 3 into label 1
        >>> transform = tio.RemapLabels({2: 1, 3: 1})
        >>> # Swap labels 1 and 2
        >>> transform = tio.RemapLabels({1: 2, 2: 1})
    """

    def __init__(
        self,
        remapping: dict[int, int],
        **kwargs: Any,
    ) -> None:
        super().__init__(**kwargs)
        self.remapping = remapping

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

    def apply_transform(
        self,
        batch: SubjectsBatch,
        params: dict[str, Any],
    ) -> SubjectsBatch:
        """Remap labels in each label map."""
        remapping = params["remapping"]
        for _name, img_batch in batch.images.items():
            if not issubclass(img_batch._image_class, LabelMap):
                continue
            data = img_batch.data.clone()
            for old, new in remapping.items():
                data[img_batch.data == old] = new
            img_batch.data = data
        return batch

    @property
    def invertible(self) -> bool:
        """Whether this transform can be inverted."""
        return True

    def inverse(self, params: dict[str, Any]) -> RemapLabels:
        """Invert by swapping keys and values."""
        remapping = params["remapping"]
        inverse_remapping = {v: k for k, v in remapping.items()}
        return RemapLabels(remapping=inverse_remapping, copy=False)

supports_per_instance_params property

Whether this transform can sample parameters per batch element.

Defaults to False. Transforms that implement per-instance parameter sampling override this to return True. When False, the transform always uses batch-shared parameters regardless of the per_instance flag, preserving the legacy behavior.

supports_per_instance_p property

Whether this transform can gate each batch element independently.

Defaults to False. Shape-preserving transforms that implement per-element probability override this to return True. Shape-changing transforms must leave it False because masked and unmasked elements would have incompatible shapes.

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)
    # When per-element gating is active, the transform handles the
    # probability itself (masked-out elements get identity params),
    # so skip the batch-wide coin flip here. Apply iff rand < p, so
    # p=0 is always a no-op and p=1 always applies.
    if not self._per_instance_p_active(batch) and 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, unless every element was gated out by
    # per-element probability: that is an exact no-op, and recording it
    # would let history replay (e.g. an invertible spatial transform)
    # trigger an unnecessary identity resample.
    if not _all_elements_gated_out(params):
        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

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

apply_transform(batch, params)

Remap labels in each label map.

Source code in src/torchio/transforms/label/remap_labels.py 
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def apply_transform(
    self,
    batch: SubjectsBatch,
    params: dict[str, Any],
) -> SubjectsBatch:
    """Remap labels in each label map."""
    remapping = params["remapping"]
    for _name, img_batch in batch.images.items():
        if not issubclass(img_batch._image_class, LabelMap):
            continue
        data = img_batch.data.clone()
        for old, new in remapping.items():
            data[img_batch.data == old] = new
        img_batch.data = data
    return batch

inverse(params)

Invert by swapping keys and values.

Source code in src/torchio/transforms/label/remap_labels.py 
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def inverse(self, params: dict[str, Any]) -> RemapLabels:
    """Invert by swapping keys and values."""
    remapping = params["remapping"]
    inverse_remapping = {v: k for k, v in remapping.items()}
    return RemapLabels(remapping=inverse_remapping, copy=False)