Recoded _max_value method using a dictionary (#5566)

* Removed _max_value method and added a dictionary

Related to #5502

* Addressed failing tests and restored _max_value method

* Added xfailing test to switch quicker

* Switch to if/else impl

Author: vfdev-5

test/test_transforms.py

@@ -1486,6 +1486,15 @@ def test_max_value(dtype):
     # self.assertGreater(F_t._max_value(dtype), torch.finfo(dtype).max)
 
 
+@pytest.mark.xfail(
+    reason="torch.iinfo() is not supported by torchscript. See https://github.com/pytorch/pytorch/issues/41492."
+)
+def test_max_value_iinfo():
+    @torch.jit.script
+    def max_value(image: torch.Tensor) -> int:
+        return 1 if image.is_floating_point() else torch.iinfo(image.dtype).max
+
+
 @pytest.mark.parametrize("should_vflip", [True, False])
 @pytest.mark.parametrize("single_dim", [True, False])
 def test_ten_crop(should_vflip, single_dim):

torchvision/transforms/functional_tensor.py

@@ -44,22 +44,19 @@ def get_image_num_channels(img: Tensor) -> int:
     raise TypeError(f"Input ndim should be 2 or more. Got {img.ndim}")
 
 
-def _max_value(dtype: torch.dtype) -> float:
-    # TODO: replace this method with torch.iinfo when it gets torchscript support.
-    # https://github.com/pytorch/pytorch/issues/41492
-
-    a = torch.tensor(2, dtype=dtype)
-    signed = 1 if torch.tensor(0, dtype=dtype).is_signed() else 0
-    bits = 1
-    max_value = torch.tensor(-signed, dtype=torch.long)
-    while True:
-        next_value = a.pow(bits - signed).sub(1)
-        if next_value > max_value:
-            max_value = next_value
-            bits *= 2
-        else:
-            break
-    return max_value.item()
+def _max_value(dtype: torch.dtype) -> int:
+    if dtype == torch.uint8:
+        return int(2 ** 8) - 1
+    elif dtype == torch.int8:
+        return int(2 ** 7) - 1
+    elif dtype == torch.int16:
+        return int(2 ** 15) - 1
+    elif dtype == torch.int32:
+        return int(2 ** 31) - 1
+    elif dtype == torch.int64:
+        return int(2 ** 63) - 1
+    else:
+        return 1
 
 
 def _assert_channels(img: Tensor, permitted: List[int]) -> None:
@@ -91,19 +88,19 @@ def convert_image_dtype(image: torch.Tensor, dtype: torch.dtype = torch.float) -
         # `max + 1 - epsilon` provides more evenly distributed mapping of
         # ranges of floats to ints.
         eps = 1e-3
-        max_val = _max_value(dtype)
+        max_val = float(_max_value(dtype))
         result = image.mul(max_val + 1.0 - eps)
         return result.to(dtype)
     else:
-        input_max = _max_value(image.dtype)
+        input_max = float(_max_value(image.dtype))
 
         # int to float
         # TODO: replace with dtype.is_floating_point when torchscript supports it
         if torch.tensor(0, dtype=dtype).is_floating_point():
             image = image.to(dtype)
             return image / input_max
 
-        output_max = _max_value(dtype)
+        output_max = float(_max_value(dtype))
 
         # int to int
         if input_max > output_max: