Fix #8775: handle torch.Tensor input in compute_shape_offset

tests/data/utils/test_compute_shape_offset.py

@@ -19,8 +19,8 @@
 from monai.data.utils import compute_shape_offset
 
 
-class TestComputeShapeOffsetRegression(unittest.TestCase):
-    """Regression tests for `compute_shape_offset` input-shape handling."""
+class TestComputeShapeOffset(unittest.TestCase):
+    """Unit tests for :func:`monai.data.utils.compute_shape_offset`."""
 
     def test_pytorch_size_input(self):
         """Validate `torch.Size` input produces expected shape and offset.
@@ -42,6 +42,39 @@ def test_pytorch_size_input(self):
         # 3. Prove it successfully processed the shape by checking its length
         self.assertEqual(len(shape), 3)
 
+    def setUp(self):
+        """Set up a 4x4 identity affine used across all test cases."""
+        self.affine = np.eye(4)
+
+    def test_numpy_array_input(self):
+        """Verify compute_shape_offset accepts a numpy array as spatial_shape."""
+        shape = np.array([64, 64, 64])
+        out_shape, _ = compute_shape_offset(shape, self.affine, self.affine)
+        self.assertEqual(len(out_shape), 3)
+
+    def test_list_input(self):
+        """Verify compute_shape_offset accepts a plain list as spatial_shape."""
+        shape = [64, 64, 64]
+        out_shape, _ = compute_shape_offset(shape, self.affine, self.affine)
+        self.assertEqual(len(out_shape), 3)
+
+    def test_torch_tensor_input(self):
+        """Verify compute_shape_offset accepts a torch.Tensor as spatial_shape.
+
+        This path broke in PyTorch >= 2.9 because np.array() relied on the
+        non-tuple sequence indexing protocol that PyTorch removed. Wrapping with
+        tuple() fixes it.
+        """
+        shape = torch.tensor([64, 64, 64])
+        out_shape, _ = compute_shape_offset(shape, self.affine, self.affine)
+        self.assertEqual(len(out_shape), 3)
+
+    def test_identity_affines_preserve_shape(self):
+        """Verify that identity in/out affines produce an output shape matching the input."""
+        shape = torch.tensor([32, 48, 16])
+        out_shape, _ = compute_shape_offset(shape, self.affine, self.affine)
+        np.testing.assert_allclose(np.array(out_shape, dtype=float), shape.numpy().astype(float), atol=1e-5)
+
 
 if __name__ == "__main__":
     unittest.main()