Adding a placeholder for resampling of point clouds

Signed-off-by: Ben Murray <ben.murray@gmail.com>

monai/transforms/__init__.py

@@ -239,7 +239,7 @@
 from .lazy.array import ApplyPending
 from .lazy.dictionary import ApplyPendingd, ApplyPendingD, ApplyPendingDict
 from .lazy.functional import apply_pending
-from .lazy.utils import combine_transforms, resample
+from .lazy.utils import combine_transforms, resample_image
 from .meta_utility.dictionary import (
     FromMetaTensord,
     FromMetaTensorD,

monai/transforms/lazy/functional.py

@@ -27,7 +27,8 @@
     combine_transforms,
     is_compatible_apply_kwargs,
     kwargs_from_pending,
-    resample,
+    resample_image,
+    resample_points,
 )
 from monai.transforms.traits import LazyTrait
 from monai.transforms.transform import MapTransform
@@ -336,7 +337,7 @@ def apply_pending(data: torch.Tensor | MetaTensor, pending: list | None = None,
             # carry out an intermediate resample here due to incompatibility between arguments
             _cur_kwargs = cur_kwargs.copy()
             _cur_kwargs.update(override_kwargs)
-            data = resample(data.to(device), cumulative_xform, _cur_kwargs)
+            data = resample_image(data.to(device), cumulative_xform, _cur_kwargs)
 
         next_matrix = affine_from_pending(p)
         if next_matrix.shape[0] == 3:
@@ -345,7 +346,10 @@ def apply_pending(data: torch.Tensor | MetaTensor, pending: list | None = None,
         cumulative_xform = combine_transforms(cumulative_xform, next_matrix)
         cur_kwargs.update(new_kwargs)
     cur_kwargs.update(override_kwargs)
-    data = resample(data.to(device), cumulative_xform, cur_kwargs)
+    if data.kind() == 'pixel':
+        data = resample_image(data.to(device), cumulative_xform, cur_kwargs)
+    elif data.kind() == 'point':
+        data = resample_points(data.to(device), cumulative_xform, cur_kwargs)
     if isinstance(data, MetaTensor):
         for p in pending:
             data.push_applied_operation(p)

monai/transforms/lazy/utils.py

@@ -23,7 +23,7 @@
 from monai.transforms.utils_pytorch_numpy_unification import allclose
 from monai.utils import LazyAttr, convert_to_numpy, convert_to_tensor, look_up_option
 
-__all__ = ["resample", "combine_transforms"]
+__all__ = ["resample_image", "combine_transforms"]
 
 
 def affine_from_pending(pending_item):
@@ -91,7 +91,7 @@ def requires_interp(matrix, atol=AFFINE_TOL):
 __override_lazy_keywords = {*list(LazyAttr), "atol"}
 
 
-def resample(data: torch.Tensor, matrix: NdarrayOrTensor, kwargs: dict | None = None):
+def resample_image(data: torch.Tensor, matrix: NdarrayOrTensor, kwargs: dict | None = None):
     """
     Resample `data` using the affine transformation defined by ``matrix``.
 
@@ -173,3 +173,8 @@ def resample(data: torch.Tensor, matrix: NdarrayOrTensor, kwargs: dict | None =
     resampler.lazy = False  # resampler is a lazytransform
     with resampler.trace_transform(False):  # don't track this transform in `img`
         return resampler(img=img, **call_kwargs)
+
+
+def resample_points(data: torch.Tensor, matrix: NdarrayOrTensor, kwargs: dict | None = None):
+    # Handle all point resampling here
+    raise NotImplementedError()

tests/test_resample.py

@@ -16,7 +16,7 @@
 import torch
 from parameterized import parameterized
 
-from monai.transforms.lazy.functional import resample
+from monai.transforms.lazy.functional import resample_image
 from monai.utils import convert_to_tensor
 from tests.utils import assert_allclose, get_arange_img
 
@@ -37,12 +37,12 @@ def rotate_90_2d():
 class TestResampleFunction(unittest.TestCase):
     @parameterized.expand(RESAMPLE_FUNCTION_CASES)
     def test_resample_function_impl(self, img, matrix, expected):
-        out = resample(convert_to_tensor(img), matrix, {"lazy_shape": img.shape[1:], "lazy_padding_mode": "border"})
+        out = resample_image(convert_to_tensor(img), matrix, {"lazy_shape": img.shape[1:], "lazy_padding_mode": "border"})
         assert_allclose(out[0], expected, type_test=False)
 
         img = convert_to_tensor(img, dtype=torch.uint8)
-        out = resample(img, matrix, {"lazy_resample_mode": "auto", "lazy_dtype": torch.float})
-        out_1 = resample(img, matrix, {"lazy_resample_mode": "other value", "lazy_dtype": torch.float})
+        out = resample_image(img, matrix, {"lazy_resample_mode": "auto", "lazy_dtype": torch.float})
+        out_1 = resample_image(img, matrix, {"lazy_resample_mode": "other value", "lazy_dtype": torch.float})
         self.assertIs(out.dtype, out_1.dtype)  # testing dtype in different lazy_resample_mode