diff --git a/test/test_prototype_transforms_functional.py b/test/test_prototype_transforms_functional.py
index bcd589328c5..4661f18ffa7 100644
--- a/test/test_prototype_transforms_functional.py
+++ b/test/test_prototype_transforms_functional.py
@@ -357,7 +357,7 @@ def pad_segmentation_mask():
     for mask, padding, padding_mode in itertools.product(
         make_segmentation_masks(),
         [[1], [1, 1], [1, 1, 2, 2]],  # padding
-        ["constant", "symmetric", "edge"],  # padding mode,
+        ["constant", "symmetric", "edge", "reflect"],  # padding mode,
     ):
         if padding_mode == "symmetric" and mask.ndim not in [3, 4]:
             continue
@@ -969,15 +969,24 @@ def test_correctness_pad_segmentation_mask_on_fixed_input(device):
     torch.testing.assert_close(out_mask, expected_mask)
 
 
-@pytest.mark.parametrize("padding,padding_mode", [([1, 2, 3, 4], "constant")])
-def test_correctness_pad_segmentation_mask(padding, padding_mode):
-    def compute_expected_mask():
-        h, w = mask.shape[-2], mask.shape[-1]
+@pytest.mark.parametrize("padding", [[1, 2, 3, 4], [1], 1, 1.0, [1, 2]])
+def test_correctness_pad_segmentation_mask(padding):
+    def _parse_padding():
+        if isinstance(padding, int):
+            return [padding] * 4
+        if isinstance(padding, float):
+            return [int(padding)] * 4
+        if isinstance(padding, list):
+            if len(padding) == 1:
+                return padding * 4
+            if len(padding) == 2:
+                return padding * 2  # [left, up, right, down]
+
+        return padding
 
-        pad_left = padding[0]
-        pad_up = padding[1]
-        pad_right = padding[2]
-        pad_down = padding[3]
+    def _compute_expected_mask(padding):
+        h, w = mask.shape[-2], mask.shape[-1]
+        pad_left, pad_up, pad_right, pad_down = padding
 
         new_h = h + pad_up + pad_down
         new_w = w + pad_left + pad_right
@@ -988,8 +997,10 @@ def compute_expected_mask():
 
         return expected_mask
 
+    padding = _parse_padding()
+
     for mask in make_segmentation_masks():
-        out_mask = F.pad_segmentation_mask(mask, padding, padding_mode)
+        out_mask = F.pad_segmentation_mask(mask, padding, "constant")
 
-        expected_mask = compute_expected_mask()
+        expected_mask = _compute_expected_mask(padding)
         torch.testing.assert_close(out_mask, expected_mask)