make datapoint methods private

Author: pmeier

test/test_transforms_v2.py

@@ -1417,12 +1417,12 @@ def test_antialias_warning():
         transforms.RandomResize(10, 20)(tensor_img)
 
     with pytest.warns(UserWarning, match=match):
-        datapoints.Image(tensor_img).resized_crop(0, 0, 10, 10, (20, 20))
+        datapoints.Image(tensor_img)._resized_crop(0, 0, 10, 10, (20, 20))
 
     with pytest.warns(UserWarning, match=match):
-        datapoints.Video(tensor_video).resize((20, 20))
+        datapoints.Video(tensor_video)._resize((20, 20))
     with pytest.warns(UserWarning, match=match):
-        datapoints.Video(tensor_video).resized_crop(0, 0, 10, 10, (20, 20))
+        datapoints.Video(tensor_video)._resized_crop(0, 0, 10, 10, (20, 20))
 
     with warnings.catch_warnings():
         warnings.simplefilter("error")
@@ -1436,8 +1436,8 @@ def test_antialias_warning():
         transforms.RandomShortestSize((20, 20), antialias=True)(tensor_img)
         transforms.RandomResize(10, 20, antialias=True)(tensor_img)
 
-        datapoints.Image(tensor_img).resized_crop(0, 0, 10, 10, (20, 20), antialias=True)
-        datapoints.Video(tensor_video).resized_crop(0, 0, 10, 10, (20, 20), antialias=True)
+        datapoints.Image(tensor_img)._resized_crop(0, 0, 10, 10, (20, 20), antialias=True)
+        datapoints.Video(tensor_video)._resized_crop(0, 0, 10, 10, (20, 20), antialias=True)
 
 
 @pytest.mark.parametrize("image_type", (PIL.Image, torch.Tensor, datapoints.Image))

test/test_transforms_v2_functional.py

@@ -424,7 +424,7 @@ def test_pil_output_type(self, info, args_kwargs):
     def test_dispatch_datapoint(self, info, args_kwargs, spy_on):
         (datapoint, *other_args), kwargs = args_kwargs.load()
 
-        method_name = info.id
+        method_name = f"_{info.id}"
         method = getattr(datapoint, method_name)
         datapoint_type = type(datapoint)
         spy = spy_on(method, module=datapoint_type.__module__, name=f"{datapoint_type.__name__}.{method_name}")

test/test_transforms_v2_refactored.py

@@ -252,7 +252,7 @@ def _check_dispatcher_datapoint_signature_match(dispatcher):
     dispatcher_signature = inspect.signature(dispatcher)
     dispatcher_params = list(dispatcher_signature.parameters.values())[1:]
 
-    datapoint_method = getattr(datapoints._datapoint.Datapoint, dispatcher.__name__)
+    datapoint_method = getattr(datapoints._datapoint.Datapoint, f"_{dispatcher.__name__}")
     datapoint_signature = inspect.signature(datapoint_method)
     datapoint_params = list(datapoint_signature.parameters.values())[1:]
 

torchvision/datapoints/_bounding_box.py

@@ -98,19 +98,19 @@ def wrap_like(
     def __repr__(self, *, tensor_contents: Any = None) -> str:  # type: ignore[override]
         return self._make_repr(format=self.format, spatial_size=self.spatial_size)
 
-    def horizontal_flip(self) -> BoundingBox:
+    def _horizontal_flip(self) -> BoundingBox:
         output = self._F.horizontal_flip_bounding_box(
             self.as_subclass(torch.Tensor), format=self.format, spatial_size=self.spatial_size
         )
         return BoundingBox.wrap_like(self, output)
 
-    def vertical_flip(self) -> BoundingBox:
+    def _vertical_flip(self) -> BoundingBox:
         output = self._F.vertical_flip_bounding_box(
             self.as_subclass(torch.Tensor), format=self.format, spatial_size=self.spatial_size
         )
         return BoundingBox.wrap_like(self, output)
 
-    def resize(  # type: ignore[override]
+    def _resize(
         self,
         size: List[int],
         interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
@@ -125,19 +125,19 @@ def resize(  # type: ignore[override]
         )
         return BoundingBox.wrap_like(self, output, spatial_size=spatial_size)
 
-    def crop(self, top: int, left: int, height: int, width: int) -> BoundingBox:
+    def _crop(self, top: int, left: int, height: int, width: int) -> BoundingBox:
         output, spatial_size = self._F.crop_bounding_box(
             self.as_subclass(torch.Tensor), self.format, top=top, left=left, height=height, width=width
         )
         return BoundingBox.wrap_like(self, output, spatial_size=spatial_size)
 
-    def center_crop(self, output_size: List[int]) -> BoundingBox:
+    def _center_crop(self, output_size: List[int]) -> BoundingBox:
         output, spatial_size = self._F.center_crop_bounding_box(
             self.as_subclass(torch.Tensor), format=self.format, spatial_size=self.spatial_size, output_size=output_size
         )
         return BoundingBox.wrap_like(self, output, spatial_size=spatial_size)
 
-    def resized_crop(
+    def _resized_crop(
         self,
         top: int,
         left: int,
@@ -152,7 +152,7 @@ def resized_crop(
         )
         return BoundingBox.wrap_like(self, output, spatial_size=spatial_size)
 
-    def pad(
+    def _pad(
         self,
         padding: Union[int, Sequence[int]],
         fill: Optional[Union[int, float, List[float]]] = None,
@@ -167,7 +167,7 @@ def pad(
         )
         return BoundingBox.wrap_like(self, output, spatial_size=spatial_size)
 
-    def rotate(
+    def _rotate(
         self,
         angle: float,
         interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
@@ -185,7 +185,7 @@ def rotate(
         )
         return BoundingBox.wrap_like(self, output, spatial_size=spatial_size)
 
-    def affine(
+    def _affine(
         self,
         angle: Union[int, float],
         translate: List[float],
@@ -207,7 +207,7 @@ def affine(
         )
         return BoundingBox.wrap_like(self, output)
 
-    def perspective(
+    def _perspective(
         self,
         startpoints: Optional[List[List[int]]],
         endpoints: Optional[List[List[int]]],
@@ -225,7 +225,7 @@ def perspective(
         )
         return BoundingBox.wrap_like(self, output)
 
-    def elastic(
+    def _elastic(
         self,
         displacement: torch.Tensor,
         interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,

torchvision/datapoints/_datapoint.py

@@ -142,15 +142,13 @@ def __deepcopy__(self: D, memo: Dict[int, Any]) -> D:
         # `BoundingBox.clone()`.
         return self.detach().clone().requires_grad_(self.requires_grad)  # type: ignore[return-value]
 
-    def horizontal_flip(self) -> Datapoint:
+    def _horizontal_flip(self) -> Datapoint:
         return self
 
-    def vertical_flip(self) -> Datapoint:
+    def _vertical_flip(self) -> Datapoint:
         return self
 
-    # TODO: We have to ignore override mypy error as there is torch.Tensor built-in deprecated op: Tensor.resize
-    # https://github.com/pytorch/pytorch/blob/e8727994eb7cdb2ab642749d6549bc497563aa06/torch/_tensor.py#L588-L593
-    def resize(  # type: ignore[override]
+    def _resize(
         self,
         size: List[int],
         interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
@@ -159,13 +157,13 @@ def resize(  # type: ignore[override]
     ) -> Datapoint:
         return self
 
-    def crop(self, top: int, left: int, height: int, width: int) -> Datapoint:
+    def _crop(self, top: int, left: int, height: int, width: int) -> Datapoint:
         return self
 
-    def center_crop(self, output_size: List[int]) -> Datapoint:
+    def _center_crop(self, output_size: List[int]) -> Datapoint:
         return self
 
-    def resized_crop(
+    def _resized_crop(
         self,
         top: int,
         left: int,
@@ -177,15 +175,15 @@ def resized_crop(
     ) -> Datapoint:
         return self
 
-    def pad(
+    def _pad(
         self,
         padding: List[int],
         fill: Optional[Union[int, float, List[float]]] = None,
         padding_mode: str = "constant",
     ) -> Datapoint:
         return self
 
-    def rotate(
+    def _rotate(
         self,
         angle: float,
         interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
@@ -195,7 +193,7 @@ def rotate(
     ) -> Datapoint:
         return self
 
-    def affine(
+    def _affine(
         self,
         angle: Union[int, float],
         translate: List[float],
@@ -207,7 +205,7 @@ def affine(
     ) -> Datapoint:
         return self
 
-    def perspective(
+    def _perspective(
         self,
         startpoints: Optional[List[List[int]]],
         endpoints: Optional[List[List[int]]],
@@ -217,51 +215,54 @@ def perspective(
     ) -> Datapoint:
         return self
 
-    def elastic(
+    def _elastic(
         self,
         displacement: torch.Tensor,
         interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
         fill: _FillTypeJIT = None,
     ) -> Datapoint:
         return self
 
-    def rgb_to_grayscale(self, num_output_channels: int = 1) -> Datapoint:
+    def _rgb_to_grayscale(self, num_output_channels: int = 1) -> Datapoint:
         return self
 
-    def adjust_brightness(self, brightness_factor: float) -> Datapoint:
+    def _adjust_brightness(self, brightness_factor: float) -> Datapoint:
         return self
 
-    def adjust_saturation(self, saturation_factor: float) -> Datapoint:
+    def _adjust_saturation(self, saturation_factor: float) -> Datapoint:
         return self
 
-    def adjust_contrast(self, contrast_factor: float) -> Datapoint:
+    def _adjust_contrast(self, contrast_factor: float) -> Datapoint:
         return self
 
-    def adjust_sharpness(self, sharpness_factor: float) -> Datapoint:
+    def _adjust_sharpness(self, sharpness_factor: float) -> Datapoint:
         return self
 
-    def adjust_hue(self, hue_factor: float) -> Datapoint:
+    def _adjust_hue(self, hue_factor: float) -> Datapoint:
         return self
 
-    def adjust_gamma(self, gamma: float, gain: float = 1) -> Datapoint:
+    def _adjust_gamma(self, gamma: float, gain: float = 1) -> Datapoint:
         return self
 
-    def posterize(self, bits: int) -> Datapoint:
+    def _posterize(self, bits: int) -> Datapoint:
         return self
 
-    def solarize(self, threshold: float) -> Datapoint:
+    def _solarize(self, threshold: float) -> Datapoint:
         return self
 
-    def autocontrast(self) -> Datapoint:
+    def _autocontrast(self) -> Datapoint:
         return self
 
-    def equalize(self) -> Datapoint:
+    def _equalize(self) -> Datapoint:
         return self
 
-    def invert(self) -> Datapoint:
+    def _invert(self) -> Datapoint:
         return self
 
-    def gaussian_blur(self, kernel_size: List[int], sigma: Optional[List[float]] = None) -> Datapoint:
+    def _gaussian_blur(self, kernel_size: List[int], sigma: Optional[List[float]] = None) -> Datapoint:
+        return self
+
+    def _normalize(self, mean: List[float], std: List[float], inplace: bool = False) -> Datapoint:
         return self