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Clamp

Bases: IntensityTransform

Clamp intensity values into the range \([a, b]\).

Wraps torch.clamp.

Parameters:

Name Type Description Default
out_min float | None

Minimum value \(a\). None means no lower bound.

 None
out_max float | None

Maximum value \(b\). None means no upper bound.

 None
**kwargs Any

See Transform.

 {}

Examples:

>>> import torchio as tio
>>> # CT windowing: clip to [-1000, 1000] Hounsfield units
>>> clamp = tio.Clamp(out_min=-1000, out_max=1000)
>>> # Clip negative values only
>>> clamp = tio.Clamp(out_min=0)
Source code in src/torchio/transforms/intensity/clamp.py 
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class Clamp(IntensityTransform):
    r"""Clamp intensity values into the range $[a, b]$.

    Wraps [`torch.clamp`][torch.clamp].

    Args:
        out_min: Minimum value $a$. `None` means no lower bound.
        out_max: Maximum value $b$. `None` means no upper bound.
        **kwargs: See [`Transform`][torchio.Transform].

    Examples:
        >>> import torchio as tio
        >>> # CT windowing: clip to [-1000, 1000] Hounsfield units
        >>> clamp = tio.Clamp(out_min=-1000, out_max=1000)
        >>> # Clip negative values only
        >>> clamp = tio.Clamp(out_min=0)
    """

    def __init__(
        self,
        *,
        out_min: float | None = None,
        out_max: float | None = None,
        **kwargs: Any,
    ) -> None:
        super().__init__(**kwargs)
        if out_min is not None and out_max is not None and out_min > out_max:
            msg = f"out_min ({out_min}) must be <= out_max ({out_max})"
            raise ValueError(msg)
        self.out_min = out_min
        self.out_max = out_max

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

    def apply_transform(
        self,
        batch: SubjectsBatch,
        params: dict[str, Any],
    ) -> SubjectsBatch:
        """Clamp each selected image."""
        out_min = params["out_min"]
        out_max = params["out_max"]
        for _name, img_batch in self._get_images(batch).items():
            img_batch.data = img_batch.data.clamp(min=out_min, max=out_max)
        return batch

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

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

apply_transform(batch, params)

Clamp each selected image.

Source code in src/torchio/transforms/intensity/clamp.py 
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def apply_transform(
    self,
    batch: SubjectsBatch,
    params: dict[str, Any],
) -> SubjectsBatch:
    """Clamp each selected image."""
    out_min = params["out_min"]
    out_max = params["out_max"]
    for _name, img_batch in self._get_images(batch).items():
        img_batch.data = img_batch.data.clamp(min=out_min, max=out_max)
    return batch