diff --git a/test/test_functional_tensor.py b/test/test_functional_tensor.py
index 0c2194e0f7b..89af6dce5d7 100644
--- a/test/test_functional_tensor.py
+++ b/test/test_functional_tensor.py
@@ -259,6 +259,7 @@ def test_pad(self):
                     {"padding_mode": "constant", "fill": 20},
                     {"padding_mode": "edge"},
                     {"padding_mode": "reflect"},
+                    {"padding_mode": "symmetric"},
                 ]
                 for kwargs in configs:
                     pad_tensor = F_t.pad(tensor, pad, **kwargs)
@@ -278,6 +279,9 @@ def test_pad(self):
                     pad_tensor_script = script_fn(tensor, script_pad, **kwargs)
                     self.assertTrue(pad_tensor.equal(pad_tensor_script), msg="{}, {}".format(pad, kwargs))
 
+        with self.assertRaises(ValueError, msg="Padding can not be negative for symmetric padding_mode"):
+            F_t.pad(tensor, (-2, -3), padding_mode="symmetric")
+
 
 if __name__ == '__main__':
     unittest.main()
diff --git a/torchvision/transforms/functional_tensor.py b/torchvision/transforms/functional_tensor.py
index 6ea3ccb2956..ad522cd7e11 100644
--- a/torchvision/transforms/functional_tensor.py
+++ b/torchvision/transforms/functional_tensor.py
@@ -346,6 +346,29 @@ def _hsv2rgb(img):
     return torch.einsum("ijk, xijk -> xjk", mask.to(dtype=img.dtype), a4)
 
 
+def _pad_symmetric(img: Tensor, padding: List[int]) -> Tensor:
+    # padding is left, right, top, bottom
+    in_sizes = img.size()
+
+    x_indices = [i for i in range(in_sizes[-1])]  # [0, 1, 2, 3, ...]
+    left_indices = [i for i in range(padding[0] - 1, -1, -1)]  # e.g. [3, 2, 1, 0]
+    right_indices = [-(i + 1) for i in range(padding[1])]  # e.g. [-1, -2, -3]
+    x_indices = torch.tensor(left_indices + x_indices + right_indices)
+
+    y_indices = [i for i in range(in_sizes[-2])]
+    top_indices = [i for i in range(padding[2] - 1, -1, -1)]
+    bottom_indices = [-(i + 1) for i in range(padding[3])]
+    y_indices = torch.tensor(top_indices + y_indices + bottom_indices)
+
+    ndim = img.ndim
+    if ndim == 3:
+        return img[:, y_indices[:, None], x_indices[None, :]]
+    elif ndim == 4:
+        return img[:, :, y_indices[:, None], x_indices[None, :]]
+    else:
+        raise RuntimeError("Symmetric padding of N-D tensors are not supported yet")
+
+
 def pad(img: Tensor, padding: List[int], fill: int = 0, padding_mode: str = "constant") -> Tensor:
     r"""Pad the given Tensor Image on all sides with specified padding mode and fill value.
 
@@ -370,6 +393,11 @@ def pad(img: Tensor, padding: List[int], fill: int = 0, padding_mode: str = "con
                        padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
                        will result in [3, 2, 1, 2, 3, 4, 3, 2]
 
+            - symmetric: pads with reflection of image (repeating the last value on the edge)
+
+                         padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
+                         will result in [2, 1, 1, 2, 3, 4, 4, 3]
+
     Returns:
         Tensor: Padded image.
     """
@@ -390,8 +418,8 @@ def pad(img: Tensor, padding: List[int], fill: int = 0, padding_mode: str = "con
         raise ValueError("Padding must be an int or a 1, 2, or 4 element tuple, not a " +
                          "{} element tuple".format(len(padding)))
 
-    if padding_mode not in ["constant", "edge", "reflect"]:
-        raise ValueError("Padding mode should be either constant, edge or reflect")
+    if padding_mode not in ["constant", "edge", "reflect", "symmetric"]:
+        raise ValueError("Padding mode should be either constant, edge, reflect or symmetric")
 
     if isinstance(padding, int):
         if torch.jit.is_scripting():
@@ -413,6 +441,11 @@ def pad(img: Tensor, padding: List[int], fill: int = 0, padding_mode: str = "con
     if padding_mode == "edge":
         # remap padding_mode str
         padding_mode = "replicate"
+    elif padding_mode == "symmetric":
+        # route to another implementation
+        if p[0] < 0 or p[1] < 0 or p[2] < 0 or p[3] < 0:  # no any support for torch script
+            raise ValueError("Padding can not be negative for symmetric padding_mode")
+        return _pad_symmetric(img, p)
 
     need_squeeze = False
     if img.ndim < 4: