fix(transforms): make Crop.compute_slices torch.compile-friendly

monai/transforms/croppad/array.py

@@ -342,6 +342,28 @@ def compute_pad_width(self, spatial_shape: Sequence[int]) -> tuple[tuple[int, in
         return spatial_pad.compute_pad_width(spatial_shape)
 
 
+def _to_int_list(data: Sequence[int] | int | NdarrayOrTensor) -> list[int]:
+    """Coerce an ROI spec (scalar, sequence, tensor or ndarray) to a list of Python ints."""
+    if isinstance(data, (torch.Tensor, np.ndarray)):
+        data = data.tolist()
+    if isinstance(data, (str, bytes)):  # a str is a Sequence, guard so it is not iterated into digits
+        raise TypeError(f"ROI spec must be numeric, got {type(data).__name__}.")
+    if isinstance(data, Sequence):
+        return [int(i) for i in data]
+    return [int(data)]
+
+
+def _broadcast_int_pair(
+    a: Sequence[int] | int | NdarrayOrTensor, b: Sequence[int] | int | NdarrayOrTensor
+) -> tuple[list[int], list[int]]:
+    """Coerce a pair of ROI specs to two equal-length int lists, broadcasting a scalar to match."""
+    list_a, list_b = _to_int_list(a), _to_int_list(b)
+    n = max(len(list_a), len(list_b))
+    if len(list_a) not in (1, n) or len(list_b) not in (1, n):
+        raise ValueError(f"ROI specs must have matching lengths or be scalar, got {len(list_a)} and {len(list_b)}.")
+    return (list_a * n if len(list_a) == 1 else list_a), (list_b * n if len(list_b) == 1 else list_b)
+
+
 class Crop(InvertibleTransform, LazyTransform):
     """
     Perform crop operations on the input image.
@@ -379,31 +401,22 @@ def compute_slices(
             roi_slices: list of slices for each of the spatial dimensions.
 
         """
-        roi_start_t: torch.Tensor
-
         if roi_slices:
             if not all(s.step is None or s.step == 1 for s in roi_slices):
                 raise ValueError(f"only slice steps of 1/None are currently supported, got {roi_slices}.")
             return ensure_tuple(roi_slices)
         else:
             if roi_center is not None and roi_size is not None:
-                roi_center_t = convert_to_tensor(data=roi_center, dtype=torch.int16, wrap_sequence=True, device="cpu")
-                roi_size_t = convert_to_tensor(data=roi_size, dtype=torch.int16, wrap_sequence=True, device="cpu")
-                _zeros = torch.zeros_like(roi_center_t)
-                half = torch.divide(roi_size_t, 2, rounding_mode="floor")
-                roi_start_t = torch.maximum(roi_center_t - half, _zeros)
-                roi_end_t = torch.maximum(roi_start_t + roi_size_t, roi_start_t)
+                centers, sizes = _broadcast_int_pair(roi_center, roi_size)
+                starts = [max(c - s // 2, 0) for c, s in zip(centers, sizes)]
+                ends = [st + s for st, s in zip(starts, sizes)]
             else:
                 if roi_start is None or roi_end is None:
                     raise ValueError("please specify either roi_center, roi_size or roi_start, roi_end.")
-                roi_start_t = convert_to_tensor(data=roi_start, dtype=torch.int16, wrap_sequence=True)
-                roi_start_t = torch.maximum(roi_start_t, torch.zeros_like(roi_start_t))
-                roi_end_t = convert_to_tensor(data=roi_end, dtype=torch.int16, wrap_sequence=True)
-                roi_end_t = torch.maximum(roi_end_t, roi_start_t)
-            # convert to slices (accounting for 1d)
-            if roi_start_t.numel() == 1:
-                return ensure_tuple([slice(int(roi_start_t.item()), int(roi_end_t.item()))])
-            return ensure_tuple([slice(int(s), int(e)) for s, e in zip(roi_start_t.tolist(), roi_end_t.tolist())])
+                starts, ends = _broadcast_int_pair(roi_start, roi_end)
+                starts = [max(s, 0) for s in starts]
+            # clamp each end to its own start so no slice has negative width
+            return ensure_tuple([slice(s, max(e, s)) for s, e in zip(starts, ends)])
 
     def __call__(  # type: ignore[override]
         self, img: torch.Tensor, slices: tuple[slice, ...], lazy: bool | None = None

tests/transforms/test_center_spatial_crop.py

@@ -14,10 +14,14 @@
 import unittest
 
 import numpy as np
+import torch
 from parameterized import parameterized
 
+from monai.data.meta_obj import get_track_meta, set_track_meta
 from monai.transforms import CenterSpatialCrop
+from monai.transforms.croppad.array import Crop
 from tests.croppers import CropTest
+from tests.test_utils import SkipIfBeforePyTorchVersion
 
 TEST_SHAPES = [
     [{"roi_size": [2, 2, -1]}, (3, 3, 3, 3), (3, 2, 2, 3), True],
@@ -50,6 +54,29 @@ def test_value(self, input_param, input_arr, expected_arr):
     def test_pending_ops(self, input_param, input_shape, _, align_corners):
         self.crop_test_pending_ops(input_param, input_shape, align_corners)
 
+    def test_compute_slices_broadcast(self):
+        self.assertEqual(Crop.compute_slices(roi_center=2, roi_size=(4, 6, 8)), (slice(0, 4), slice(0, 6), slice(0, 8)))
+        self.assertEqual(Crop.compute_slices(roi_start=1, roi_end=(3, 5, 7)), (slice(1, 3), slice(1, 5), slice(1, 7)))
+        with self.assertRaises(ValueError):
+            Crop.compute_slices(roi_center=(2, 3), roi_size=(4, 5, 6))
+        with self.assertRaises(ValueError):
+            Crop.compute_slices(roi_start=(1, 2), roi_end=(3, 5, 7))
+        with self.assertRaises(TypeError):
+            Crop.compute_slices(roi_center="10", roi_size=(4, 6))
+
+    @SkipIfBeforePyTorchVersion((2, 1))
+    def test_torch_compile(self):
+        prev_track_meta = get_track_meta()
+        set_track_meta(False)
+        try:
+            # eager backend traces the transform without needing the Inductor C++ compiler
+            cropper = torch.compile(CenterSpatialCrop(roi_size=(1, 16, 16)), backend="eager")
+            img = torch.rand(1, 1, 32, 32, dtype=torch.float32)
+            self.assertEqual(tuple(cropper(img).shape), (1, 1, 16, 16))
+        finally:
+            set_track_meta(prev_track_meta)
+            torch._dynamo.reset()
+
 
 if __name__ == "__main__":
     unittest.main()