diff --git a/test/test_transforms.py b/test/test_transforms.py
index b9fa620e187..4d857ae484a 100644
--- a/test/test_transforms.py
+++ b/test/test_transforms.py
@@ -288,6 +288,15 @@ def test_pil_to_tensor(self, channels):
         expected_output = (input_data * 255).byte()
         torch.testing.assert_close(output, expected_output)
 
+        for mode in ("I;16", "I;16B"):
+            input_data = torch.tensor([[1, 258], [3, 4]], dtype=torch.int16)
+            array = input_data.numpy()
+            if mode == "I;16B":
+                array = array.byteswap()
+            img = Image.fromarray(array, mode=mode)
+            output = trans(img)
+            torch.testing.assert_close(input_data.unsqueeze(0), output)
+
         # separate test for mode '1' PIL images
         input_data = torch.ByteTensor(1, height, width).bernoulli_()
         img = transforms.ToPILImage()(input_data.mul(255)).convert("1")
diff --git a/torchvision/transforms/functional.py b/torchvision/transforms/functional.py
index 7b950b0c45b..4550ace4d24 100644
--- a/torchvision/transforms/functional.py
+++ b/torchvision/transforms/functional.py
@@ -206,7 +206,13 @@ def pil_to_tensor(pic: Any) -> Tensor:
         return torch.as_tensor(nppic)
 
     # handle PIL Image
-    img = torch.as_tensor(np.array(pic, copy=True))
+    mode_to_nptype = {
+        "I": np.int32,
+        "I;16": np.int16,
+        "I;16B": np.int16,
+        "F": np.float32,
+    }
+    img = torch.as_tensor(np.array(pic, mode_to_nptype.get(pic.mode, np.uint8), copy=True))
     img = img.view(pic.size[1], pic.size[0], F_pil.get_image_num_channels(pic))
     # put it from HWC to CHW format
     img = img.permute((2, 0, 1))