Add explicit spatial_ndim tracking to MetaTensor

monai/data/__init__.py

@@ -71,7 +71,7 @@
     monai_to_itk_ddf,
 )
 from .meta_obj import MetaObj, get_track_meta, set_track_meta
-from .meta_tensor import MetaTensor
+from .meta_tensor import MetaTensor, get_spatial_ndim
 from .samplers import DistributedSampler, DistributedWeightedRandomSampler
 from .synthetic import create_test_image_2d, create_test_image_3d
 from .test_time_augmentation import TestTimeAugmentation

monai/data/meta_obj.py

@@ -24,6 +24,9 @@
 
 _TRACK_META = True
 
+# Default number of spatial dimensions for medical imaging (3D volumetric data)
+_DEFAULT_SPATIAL_NDIM = 3
+
 __all__ = ["get_track_meta", "set_track_meta", "MetaObj"]
 
 
@@ -84,6 +87,7 @@ def __init__(self) -> None:
         self._applied_operations: list = MetaObj.get_default_applied_operations()
         self._pending_operations: list = MetaObj.get_default_applied_operations()  # the same default as applied_ops
         self._is_batch: bool = False
+        self._spatial_ndim: int = 3  # default: 3 spatial dimensions
 
     @staticmethod
     def flatten_meta_objs(*args: Iterable):

monai/data/meta_tensor.py

@@ -13,22 +13,59 @@
 
 import functools
 import warnings
-from collections.abc import Sequence
+from collections.abc import Mapping, Sequence
 from copy import deepcopy
 from typing import Any
 
 import numpy as np
 import torch
 
 import monai
-from monai.config.type_definitions import NdarrayTensor
-from monai.data.meta_obj import MetaObj, get_track_meta
-from monai.data.utils import affine_to_spacing, decollate_batch, list_data_collate, remove_extra_metadata
+from monai.config.type_definitions import NdarrayOrTensor, NdarrayTensor
+from monai.data.meta_obj import _DEFAULT_SPATIAL_NDIM, MetaObj, get_track_meta
+from monai.data.utils import affine_to_spacing, decollate_batch, is_no_channel, list_data_collate, remove_extra_metadata
 from monai.utils import look_up_option
 from monai.utils.enums import LazyAttr, MetaKeys, PostFix, SpaceKeys
 from monai.utils.type_conversion import convert_data_type, convert_to_dst_type, convert_to_numpy, convert_to_tensor
 
-__all__ = ["MetaTensor"]
+__all__ = ["MetaTensor", "get_spatial_ndim"]
+
+
+def _normalize_spatial_ndim(spatial_ndim: int, tensor_ndim: int, no_channel: bool = False) -> int:
+    """Clamp spatial dims to a valid range for the current tensor shape."""
+    limit = max(int(tensor_ndim), 1) if no_channel else max(int(tensor_ndim) - 1, 1)
+    return max(1, min(int(spatial_ndim), limit))
+
+
+def _has_explicit_no_channel(meta: Mapping | None) -> bool:
+    return (
+        isinstance(meta, Mapping)
+        and MetaKeys.ORIGINAL_CHANNEL_DIM in meta
+        and is_no_channel(meta[MetaKeys.ORIGINAL_CHANNEL_DIM])
+    )
+
+
+def get_spatial_ndim(img: NdarrayOrTensor) -> int:
+    """Return the number of spatial dimensions assuming channel-first layout.
+
+    Uses ``MetaTensor.spatial_ndim`` when available, otherwise falls back to
+    ``img.ndim - 1``.  Always assumes channel-first (``no_channel=False``)
+    because callers run after ``EnsureChannelFirst`` has already added one.
+    """
+    if isinstance(img, MetaTensor):
+        return _normalize_spatial_ndim(img.spatial_ndim, img.ndim)
+    return img.ndim - 1
+
+
+def _is_batch_only_index(index: Any) -> bool:
+    """True when indexing pattern selects only the batch axis (e.g., ``x[0]`` or ``x[0, ...]``)."""
+    if isinstance(index, (int, np.integer)):
+        return True
+    if not isinstance(index, Sequence) or not index:
+        return False
+    if not isinstance(index[0], (int, np.integer)):
+        return False
+    return all(i in (slice(None, None, None), Ellipsis, None) for i in index[1:])
 
 
 @functools.lru_cache(None)
@@ -111,6 +148,7 @@ def __new__(
         meta: dict | None = None,
         applied_operations: list | None = None,
         *args,
+        spatial_ndim: int | None = None,
         **kwargs,
     ) -> MetaTensor:
         _kwargs = {"device": kwargs.pop("device", None), "dtype": kwargs.pop("dtype", None)} if kwargs else {}
@@ -123,6 +161,7 @@ def __init__(
         meta: dict | None = None,
         applied_operations: list | None = None,
         *_args,
+        spatial_ndim: int | None = None,
         **_kwargs,
     ) -> None:
         """
@@ -134,6 +173,8 @@ def __init__(
                 the list is typically maintained by `monai.transforms.TraceableTransform`.
                 See also: :py:class:`monai.transforms.TraceableTransform`
             _args: additional args (currently not in use in this constructor).
+            spatial_ndim: optional number of spatial dimensions. If ``None``, derived
+                from the affine matrix clamped by the tensor shape.
             _kwargs: additional kwargs (currently not in use in this constructor).
 
         Note:
@@ -158,6 +199,14 @@ def __init__(
             self.affine = self.meta[MetaKeys.AFFINE]
         else:
             self.affine = self.get_default_affine()
+        # Initialize spatial_ndim from affine matrix (source of truth), clamped by tensor shape.
+        # This cached value is kept in sync via the affine setter for hot-path performance.
+        no_channel = _has_explicit_no_channel(self.meta)
+        if spatial_ndim is not None:
+            self.spatial_ndim = _normalize_spatial_ndim(spatial_ndim, self.ndim, no_channel=no_channel)
+        elif self.affine.ndim == 2:
+            self.spatial_ndim = _normalize_spatial_ndim(self.affine.shape[-1] - 1, self.ndim, no_channel=no_channel)
+
         # applied_operations
         if applied_operations is not None:
             self.applied_operations = applied_operations
@@ -237,6 +286,7 @@ def _handle_batched(cls, ret, idx, metas, func, args, kwargs):
         if func == torch.Tensor.__getitem__:
             if idx > 0 or len(args) < 2 or len(args[0]) < 1:
                 return ret
+            full_idx = args[1]
             batch_idx = args[1][0] if isinstance(args[1], Sequence) else args[1]
             # if using e.g., `batch[:, -1]` or `batch[..., -1]`, then the
             # first element will be `slice(None, None, None)` and `Ellipsis`,
@@ -258,6 +308,8 @@ def _handle_batched(cls, ret, idx, metas, func, args, kwargs):
                 ret_meta.is_batch = False
             if hasattr(ret_meta, "__dict__"):
                 ret.__dict__ = ret_meta.__dict__.copy()
+                if _is_batch_only_index(full_idx):
+                    ret.spatial_ndim = _normalize_spatial_ndim(ret.spatial_ndim, ret.ndim)
         # `unbind` is used for `next(iter(batch))`. Also for `decollate_batch`.
         # But we only want to split the batch if the `unbind` is along the 0th dimension.
         elif func == torch.Tensor.unbind:
@@ -467,15 +519,40 @@ def affine(self) -> torch.Tensor:
 
     @affine.setter
     def affine(self, d: NdarrayTensor) -> None:
-        """Set the affine."""
-        self.meta[MetaKeys.AFFINE] = torch.as_tensor(d, device=torch.device("cpu"), dtype=torch.float64)
+        """Set the affine.
+
+        When setting a non-batched affine matrix, automatically synchronizes the cached
+        spatial_ndim attribute to maintain consistency between the affine matrix (source of truth)
+        and the cached spatial dimension count.
+        """
+        a = torch.as_tensor(d, device=torch.device("cpu"), dtype=torch.float64)
+        self.meta[MetaKeys.AFFINE] = a
+        if a.ndim == 2:  # non-batched: sync spatial_ndim from affine (source of truth)
+            no_channel = _has_explicit_no_channel(self.meta)
+            self.spatial_ndim = _normalize_spatial_ndim(a.shape[-1] - 1, self.ndim, no_channel=no_channel)
+
+    @property
+    def spatial_ndim(self) -> int:
+        """Get the number of spatial dimensions.
+
+        This value is cached for hot-path performance and is kept in sync with the affine matrix
+        via the affine setter. The affine matrix is the source of truth for spatial dimensions.
+        """
+        return getattr(self, "_spatial_ndim", _DEFAULT_SPATIAL_NDIM)
+
+    @spatial_ndim.setter
+    def spatial_ndim(self, val: int) -> None:
+        """Set the number of spatial dimensions."""
+        if val < 1:
+            raise ValueError(f"spatial_ndim must be >= 1, got {val}")
+        self._spatial_ndim = val
 
     @property
     def pixdim(self):
         """Get the spacing"""
         if self.is_batch:
-            return [affine_to_spacing(a) for a in self.affine]
-        return affine_to_spacing(self.affine)
+            return [affine_to_spacing(a, r=self.spatial_ndim) for a in self.affine]
+        return affine_to_spacing(self.affine, r=self.spatial_ndim)
 
     def peek_pending_shape(self):
         """
@@ -490,7 +567,7 @@ def peek_pending_shape(self):
 
     def peek_pending_affine(self):
         res = self.affine
-        r = len(res) - 1
+        r = res.shape[-1] - 1 if res.ndim >= 2 else self.spatial_ndim
         if r not in (2, 3):
             warnings.warn(f"Only 2d and 3d affine are supported, got {r}d input.")
         for p in self.pending_operations:
@@ -503,8 +580,10 @@ def peek_pending_affine(self):
         return res
 
     def peek_pending_rank(self):
-        a = self.pending_operations[-1].get(LazyAttr.AFFINE, None) if self.pending_operations else self.affine
-        return 1 if a is None else int(max(1, len(a) - 1))
+        if self.pending_operations:
+            a = self.pending_operations[-1].get(LazyAttr.AFFINE, None)
+            return 1 if a is None else int(max(1, len(a) - 1))
+        return self.spatial_ndim
 
     def new_empty(self, size, dtype=None, device=None, requires_grad=False):  # type: ignore[override]
         """

monai/data/utils.py

@@ -31,7 +31,7 @@
 from torch.utils.data._utils.collate import default_collate
 
 from monai.config.type_definitions import NdarrayOrTensor, NdarrayTensor, PathLike
-from monai.data.meta_obj import MetaObj
+from monai.data.meta_obj import _DEFAULT_SPATIAL_NDIM, MetaObj
 from monai.utils import (
     MAX_SEED,
     BlendMode,
@@ -432,6 +432,7 @@ def collate_meta_tensor_fn(batch, *, collate_fn_map=None):
     collated.meta = default_collate(meta_dicts)
     collated.applied_operations = [i.applied_operations or TraceKeys.NONE for i in batch]
     collated.is_batch = True
+    collated.spatial_ndim = min(getattr(batch[0], "spatial_ndim", _DEFAULT_SPATIAL_NDIM), max(collated.ndim - 1, 1))
     return collated
 
 

monai/transforms/croppad/functional.py

@@ -22,7 +22,7 @@
 
 from monai.config.type_definitions import NdarrayTensor
 from monai.data.meta_obj import get_track_meta
-from monai.data.meta_tensor import MetaTensor
+from monai.data.meta_tensor import MetaTensor, get_spatial_ndim
 from monai.data.utils import to_affine_nd
 from monai.transforms.inverse import TraceableTransform
 from monai.transforms.utils import convert_pad_mode, create_translate
@@ -132,7 +132,7 @@ def crop_or_pad_nd(img: torch.Tensor, translation_mat, spatial_size: tuple[int,
         mode: the padding mode.
         kwargs: other arguments for the `np.pad` or `torch.pad` function.
     """
-    ndim = len(img.shape) - 1
+    ndim = get_spatial_ndim(img)
     matrix_np = np.round(to_affine_nd(ndim, convert_to_numpy(translation_mat, wrap_sequence=True).copy()))
     matrix_np = to_affine_nd(len(spatial_size), matrix_np)
     cc = np.asarray(np.meshgrid(*[[0.5, x - 0.5] for x in spatial_size], indexing="ij"))

monai/transforms/intensity/array.py

@@ -26,6 +26,7 @@
 from monai.config import DtypeLike
 from monai.config.type_definitions import NdarrayOrTensor, NdarrayTensor
 from monai.data.meta_obj import get_track_meta
+from monai.data.meta_tensor import get_spatial_ndim
 from monai.data.ultrasound_confidence_map import UltrasoundConfidenceMap
 from monai.data.utils import get_random_patch, get_valid_patch_size
 from monai.networks.layers import GaussianFilter, HilbertTransform, MedianFilter, SavitzkyGolayFilter
@@ -1580,7 +1581,7 @@ def __init__(self, radius: Sequence[int] | int = 1) -> None:
     def __call__(self, img: NdarrayTensor) -> NdarrayTensor:
         img = convert_to_tensor(img, track_meta=get_track_meta())
         img_t, *_ = convert_data_type(img, torch.Tensor, dtype=torch.float)
-        spatial_dims = img_t.ndim - 1
+        spatial_dims = get_spatial_ndim(img)
         r = ensure_tuple_rep(self.radius, spatial_dims)
         median_filter_instance = MedianFilter(r, spatial_dims=spatial_dims)
         out_t: torch.Tensor = median_filter_instance(img_t)
@@ -1616,7 +1617,7 @@ def __call__(self, img: NdarrayTensor) -> NdarrayTensor:
             sigma = [torch.as_tensor(s, device=img_t.device) for s in self.sigma]
         else:
             sigma = torch.as_tensor(self.sigma, device=img_t.device)
-        gaussian_filter = GaussianFilter(img_t.ndim - 1, sigma, approx=self.approx)
+        gaussian_filter = GaussianFilter(get_spatial_ndim(img), sigma, approx=self.approx)
         out_t: torch.Tensor = gaussian_filter(img_t.unsqueeze(0)).squeeze(0)
         out, *_ = convert_to_dst_type(out_t, dst=img, dtype=out_t.dtype)
 
@@ -1673,7 +1674,7 @@ def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTen
         if not self._do_transform:
             return img
 
-        sigma = ensure_tuple_size(vals=(self.x, self.y, self.z), dim=img.ndim - 1)
+        sigma = ensure_tuple_size(vals=(self.x, self.y, self.z), dim=get_spatial_ndim(img))
         return GaussianSmooth(sigma=sigma, approx=self.approx)(img)
 
 
@@ -1723,7 +1724,7 @@ def __call__(self, img: NdarrayTensor) -> NdarrayTensor:
         img_t, *_ = convert_data_type(img, torch.Tensor, dtype=torch.float32)
 
         gf1, gf2 = (
-            GaussianFilter(img_t.ndim - 1, sigma, approx=self.approx).to(img_t.device)
+            GaussianFilter(get_spatial_ndim(img), sigma, approx=self.approx).to(img_t.device)
             for sigma in (self.sigma1, self.sigma2)
         )
         blurred_f = gf1(img_t.unsqueeze(0))
@@ -1811,8 +1812,9 @@ def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTen
 
         if self.x2 is None or self.y2 is None or self.z2 is None or self.a is None:
             raise RuntimeError("please call the `randomize()` function first.")
-        sigma1 = ensure_tuple_size(vals=(self.x1, self.y1, self.z1), dim=img.ndim - 1)
-        sigma2 = ensure_tuple_size(vals=(self.x2, self.y2, self.z2), dim=img.ndim - 1)
+        _sp = get_spatial_ndim(img)
+        sigma1 = ensure_tuple_size(vals=(self.x1, self.y1, self.z1), dim=_sp)
+        sigma2 = ensure_tuple_size(vals=(self.x2, self.y2, self.z2), dim=_sp)
         return GaussianSharpen(sigma1=sigma1, sigma2=sigma2, alpha=self.a, approx=self.approx)(img)
 
 

monai/transforms/inverse.py

@@ -213,7 +213,7 @@ def track_transform_meta(
             orig_affine = data_t.peek_pending_affine()
             orig_affine = convert_to_dst_type(orig_affine, affine, dtype=torch.float64)[0]
             try:
-                affine = orig_affine @ to_affine_nd(len(orig_affine) - 1, affine, dtype=torch.float64)
+                affine = orig_affine @ to_affine_nd(orig_affine.shape[-1] - 1, affine, dtype=torch.float64)
             except RuntimeError as e:
                 if orig_affine.ndim > 2:
                     if data_t.is_batch:

monai/transforms/lazy/functional.py

@@ -256,9 +256,11 @@ def apply_pending(data: torch.Tensor | MetaTensor, pending: list | None = None,
     if not pending:
         return data, []
 
+    _rank = data.spatial_ndim if isinstance(data, MetaTensor) else 3
+
     cumulative_xform = affine_from_pending(pending[0])
-    if cumulative_xform.shape[0] == 3:
-        cumulative_xform = to_affine_nd(3, cumulative_xform)
+    if cumulative_xform.shape[0] < _rank + 1:
+        cumulative_xform = to_affine_nd(_rank, cumulative_xform)
 
     cur_kwargs = kwargs_from_pending(pending[0])
     override_kwargs: dict[str, Any] = {}
@@ -283,8 +285,8 @@ def apply_pending(data: torch.Tensor | MetaTensor, pending: list | None = None,
             data = resample(data.to(device), cumulative_xform, _cur_kwargs)
 
         next_matrix = affine_from_pending(p)
-        if next_matrix.shape[0] == 3:
-            next_matrix = to_affine_nd(3, next_matrix)
+        if next_matrix.shape[0] < _rank + 1:
+            next_matrix = to_affine_nd(_rank, next_matrix)
 
         cumulative_xform = combine_transforms(cumulative_xform, next_matrix)
         cur_kwargs.update(new_kwargs)

monai/transforms/post/array.py

@@ -23,7 +23,7 @@
 
 from monai.config.type_definitions import NdarrayOrTensor
 from monai.data.meta_obj import get_track_meta
-from monai.data.meta_tensor import MetaTensor
+from monai.data.meta_tensor import MetaTensor, get_spatial_ndim
 from monai.networks import one_hot
 from monai.networks.layers import GaussianFilter, apply_filter, separable_filtering
 from monai.transforms.inverse import InvertibleTransform
@@ -624,7 +624,11 @@ def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
         """
         img = convert_to_tensor(img, track_meta=get_track_meta())
         img_: torch.Tensor = convert_to_tensor(img, track_meta=False)
-        spatial_dims = len(img_.shape) - 1
+        spatial_dims = get_spatial_ndim(img)
+        # Validate actual tensor shape against tracked spatial_ndim
+        actual_spatial = img_.ndim - 1  # channel-first layout
+        if actual_spatial != spatial_dims:
+            spatial_dims = actual_spatial
         img_ = img_.unsqueeze(0)  # adds a batch dim
         if spatial_dims == 2:
             kernel = torch.tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]], dtype=torch.float32)
@@ -1104,7 +1108,7 @@ def __call__(self, image: NdarrayOrTensor) -> torch.Tensor:
         image_tensor = convert_to_tensor(image, track_meta=get_track_meta())
 
         # Check/set spatial axes
-        n_spatial_dims = image_tensor.ndim - 1  # excluding the channel dimension
+        n_spatial_dims = get_spatial_ndim(image_tensor)
         valid_spatial_axes = list(range(n_spatial_dims)) + list(range(-n_spatial_dims, 0))
 
         # Check gradient axes to be valid