[proto] Fix BC for inplace arg in Normalize and RandomErasing (#6530)

Co-authored-by: Philip Meier <github.pmeier@posteo.de>

test/test_prototype_transforms.py

@@ -1004,7 +1004,13 @@ class TestRandomErasing:
 
         if p:
             mock.assert_called_once_with(
-                inpt_sentinel, i=i_sentinel, j=j_sentinel, h=h_sentinel, w=w_sentinel, v=v_sentinel
+                inpt_sentinel,
+                i=i_sentinel,
+                j=j_sentinel,
+                h=h_sentinel,
+                w=w_sentinel,
+                v=v_sentinel,
+                inplace=transform.inplace,
             )
         else:
             mock.assert_not_called()

test/test_prototype_transforms_consistency.py

@@ -88,7 +88,6 @@ CONSISTENCY_CONFIGS = [
         ],
         supports_pil=False,
         make_images_kwargs=dict(DEFAULT_MAKE_IMAGES_KWARGS, dtypes=[torch.float]),
-        removed_params=["inplace"],
     ),
     ConsistencyConfig(
         prototype_transforms.Resize,
@@ -315,7 +314,6 @@ CONSISTENCY_CONFIGS = [
             ArgsKwargs(p=1, value="random"),
         ],
         supports_pil=False,
-        removed_params=["inplace"],
     ),
     ConsistencyConfig(
         prototype_transforms.ColorJitter,

torchvision/prototype/transforms/_augment.py

@@ -23,6 +23,7 @@ class RandomErasing(_RandomApplyTransform):
         scale: Tuple[float, float] = (0.02, 0.33),
         ratio: Tuple[float, float] = (0.3, 3.3),
         value: float = 0,
+        inplace: bool = False,
     ):
         super().__init__(p=p)
         if not isinstance(value, (numbers.Number, str, tuple, list)):
@@ -40,6 +41,7 @@ class RandomErasing(_RandomApplyTransform):
         self.scale = scale
         self.ratio = ratio
         self.value = value
+        self.inplace = inplace
 
     def _get_params(self, sample: Any) -> Dict[str, Any]:
         img_c, img_h, img_w = query_chw(sample)
@@ -92,7 +94,7 @@ class RandomErasing(_RandomApplyTransform):
         self, inpt: Union[torch.Tensor, features.Image, PIL.Image.Image], params: Dict[str, Any]
     ) -> Union[torch.Tensor, features.Image, PIL.Image.Image]:
         if params["v"] is not None:
-            inpt = F.erase(inpt, **params)
+            inpt = F.erase(inpt, **params, inplace=self.inplace)
 
         return inpt
 

torchvision/prototype/transforms/_misc.py

@@ -95,13 +95,14 @@ class LinearTransformation(Transform):
 class Normalize(Transform):
     _transformed_types = (features.Image, features.is_simple_tensor)
 
-    def __init__(self, mean: Sequence[float], std: Sequence[float]):
+    def __init__(self, mean: Sequence[float], std: Sequence[float], inplace: bool = False):
         super().__init__()
         self.mean = list(mean)
         self.std = list(std)
+        self.inplace = inplace
 
     def _transform(self, inpt: Union[torch.Tensor, features._Feature], params: Dict[str, Any]) -> torch.Tensor:
-        return F.normalize(inpt, mean=self.mean, std=self.std)
+        return F.normalize(inpt, mean=self.mean, std=self.std, inplace=self.inplace)
 
     def forward(self, *inpts: Any) -> Any:
         if has_any(inpts, PIL.Image.Image):