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Transpose

Bases: SpatialTransform

Swap the first and last spatial dimensions.

Transforms an image of shape \((C, I, J, K)\) into \((C, K, J, I)\). The affine matrix is updated to reflect the reordering so that world coordinates remain consistent.

This is the v2 equivalent of v1's Transpose, which reversed the orientation string. The transform is its own inverse.

Parameters:

Name Type Description Default
**kwargs Any

See Transform.

 {}

Examples:

>>> import torchio as tio
>>> transform = tio.Transpose()
Source code in src/torchio/transforms/spatial/transpose.py 
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class Transpose(SpatialTransform):
    r"""Swap the first and last spatial dimensions.

    Transforms an image of shape $(C, I, J, K)$ into $(C, K, J, I)$.
    The affine matrix is updated to reflect the reordering so that
    world coordinates remain consistent.

    This is the v2 equivalent of v1's `Transpose`, which reversed
    the orientation string.  The transform is its own inverse.

    Args:
        **kwargs: See [`Transform`][torchio.Transform].

    Examples:
        >>> import torchio as tio
        >>> transform = tio.Transpose()
    """

    def __init__(self, **kwargs: Any) -> None:
        super().__init__(**kwargs)

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

    def apply_transform(
        self,
        batch: SubjectsBatch,
        params: dict[str, Any],
    ) -> SubjectsBatch:
        """Swap first and last spatial axes for all images."""
        for _name, img_batch in batch.images.items():
            # Swap axes 2 (I) and 4 (K) in the (B, C, I, J, K) tensor.
            img_batch.data = img_batch.data.permute(0, 1, 4, 3, 2).contiguous()
            # Update affines: swap columns 0 and 2 (I↔K).
            for affine in img_batch.affines:
                m = affine._matrix.clone()
                affine._matrix[:, 0] = m[:, 2]
                affine._matrix[:, 2] = m[:, 0]
        return batch

    @property
    def invertible(self) -> bool:
        """Transpose is its own inverse."""
        return True

    def inverse(self, params: dict[str, Any]) -> Transpose:
        """Transposing twice is identity."""
        return Transpose(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

Transpose is its own inverse.

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

apply_transform(batch, params)

Swap first and last spatial axes for all images.

Source code in src/torchio/transforms/spatial/transpose.py 
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def apply_transform(
    self,
    batch: SubjectsBatch,
    params: dict[str, Any],
) -> SubjectsBatch:
    """Swap first and last spatial axes for all images."""
    for _name, img_batch in batch.images.items():
        # Swap axes 2 (I) and 4 (K) in the (B, C, I, J, K) tensor.
        img_batch.data = img_batch.data.permute(0, 1, 4, 3, 2).contiguous()
        # Update affines: swap columns 0 and 2 (I↔K).
        for affine in img_batch.affines:
            m = affine._matrix.clone()
            affine._matrix[:, 0] = m[:, 2]
            affine._matrix[:, 2] = m[:, 0]
    return batch

inverse(params)

Transposing twice is identity.

Source code in src/torchio/transforms/spatial/transpose.py 
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def inverse(self, params: dict[str, Any]) -> Transpose:
    """Transposing twice is identity."""
    return Transpose(copy=False)