Register GlobalMutualInformationLoss bin_centers as buffer

Signed-off-by: ugbotueferhire <ugbotueferhire@gmail.com>

Author: ugbotueferhire

monai/losses/image_dissimilarity.py

@@ -233,9 +233,11 @@ def __init__(
         self.kernel_type = look_up_option(kernel_type, ["gaussian", "b-spline"])
         self.num_bins = num_bins
         self.kernel_type = kernel_type
+        self.bin_centers: torch.Tensor | None
+        self.register_buffer("bin_centers", None, persistent=False)
         if self.kernel_type == "gaussian":
             self.preterm = 1 / (2 * sigma**2)
-            self.bin_centers = bin_centers[None, None, ...]
+            self.register_buffer("bin_centers", bin_centers[None, None, ...], persistent=False)
         self.smooth_nr = float(smooth_nr)
         self.smooth_dr = float(smooth_dr)
 
@@ -314,6 +316,8 @@ def parzen_windowing_gaussian(self, img: torch.Tensor) -> tuple[torch.Tensor, to
         """
         img = torch.clamp(img, 0, 1)
         img = img.reshape(img.shape[0], -1, 1)  # (batch, num_sample, 1)
+        if self.bin_centers is None:
+            raise ValueError("bin_centers must be defined for gaussian parzen windowing.")
         weight = torch.exp(
             -self.preterm.to(img) * (img - self.bin_centers.to(img)) ** 2
         )  # (batch, num_sample, num_bin)

tests/losses/image_dissimilarity/test_global_mutual_information_loss.py

@@ -116,6 +116,25 @@ def transformation(translate_params=(0.0, 0.0, 0.0), rotate_params=(0.0, 0.0, 0.
 
 
 class TestGlobalMutualInformationLossIll(unittest.TestCase):
+    def test_gaussian_bin_centers_registered_buffer(self):
+        loss = GlobalMutualInformationLoss(kernel_type="gaussian", num_bins=16)
+
+        self.assertIn("bin_centers", dict(loss.named_buffers()))
+        self.assertIsNotNone(loss.bin_centers)
+        self.assertFalse(loss.bin_centers.requires_grad)
+
+        loss = loss.to(dtype=torch.float64)
+        self.assertEqual(loss.bin_centers.dtype, torch.float64)
+
+        if torch.cuda.is_available():
+            loss = loss.to(device="cuda:0")
+            self.assertEqual(loss.bin_centers.device, torch.device("cuda:0"))
+
+    def test_b_spline_bin_centers_exists_as_none(self):
+        loss = GlobalMutualInformationLoss(kernel_type="b-spline")
+
+        self.assertIsNone(loss.bin_centers)
+
     @parameterized.expand(
         [
             (torch.ones((1, 2), dtype=torch.float), torch.ones((1, 3), dtype=torch.float)),  # mismatched_simple_dims