extend support of posterize to all integer and floating dtypes (#6847)

* extend support of posterize to all integer and floating dtypes

* remove raise

* revert to fixed value range for integer dtypes

test/prototype_transforms_kernel_infos.py

@@ -1446,16 +1446,14 @@ _POSTERIZE_BITS = [1, 4, 8]
 
 def sample_inputs_posterize_image_tensor():
     for image_loader in make_image_loaders(
-        sizes=["random"], color_spaces=(features.ColorSpace.GRAY, features.ColorSpace.RGB), dtypes=[torch.uint8]
+        sizes=["random"], color_spaces=(features.ColorSpace.GRAY, features.ColorSpace.RGB)
     ):
         yield ArgsKwargs(image_loader, bits=_POSTERIZE_BITS[0])
 
 
 def reference_inputs_posterize_image_tensor():
     for image_loader, bits in itertools.product(
-        make_image_loaders(
-            color_spaces=(features.ColorSpace.GRAY, features.ColorSpace.RGB), extra_dims=[()], dtypes=[torch.uint8]
-        ),
+        make_image_loaders(color_spaces=(features.ColorSpace.GRAY, features.ColorSpace.RGB), extra_dims=[()]),
         _POSTERIZE_BITS,
     ):
         yield ArgsKwargs(image_loader, bits=bits)

torchvision/prototype/transforms/functional/_color.py

@@ -289,7 +289,18 @@ def adjust_gamma(inpt: features.InputTypeJIT, gamma: float, gain: float = 1) ->
         return adjust_gamma_image_pil(inpt, gamma=gamma, gain=gain)
 
 
-posterize_image_tensor = _FT.posterize
+def posterize_image_tensor(image: torch.Tensor, bits: int) -> torch.Tensor:
+    if bits > 8:
+        return image
+
+    if image.is_floating_point():
+        levels = 1 << bits
+        return image.mul(levels).floor_().clamp_(0, levels - 1).div_(levels)
+    else:
+        mask = ((1 << bits) - 1) << (8 - bits)
+        return image & mask
+
+
 posterize_image_pil = _FP.posterize