cleanup features / transforms feature branch

test/test_prototype_transforms_kernels.py

@@ -170,11 +170,11 @@ def resize_image():
 def resize_bounding_box():
     for bounding_box in make_bounding_boxes():
         height, width = bounding_box.image_size
-        for new_image_size in [
+        for size in [
             (height, width),
             (int(height * 0.75), int(width * 1.25)),
         ]:
-            yield SampleInput(bounding_box, old_image_size=bounding_box.image_size, new_image_size=new_image_size)
+            yield SampleInput(bounding_box, size=size, image_size=bounding_box.image_size)
 
 
 class TestKernelsCommon:

torchvision/prototype/features/_bounding_box.py

@@ -39,6 +39,9 @@ def __new__(
         return bounding_box
 
     def to_format(self, format: Union[str, BoundingBoxFormat]) -> BoundingBox:
+        # TODO: this is useful for developing and debugging but we should remove or at least revisit this before we
+        #  promote this out of the prototype state
+
         # import at runtime to avoid cyclic imports
         from torchvision.prototype.transforms.kernels import convert_bounding_box_format
 

torchvision/prototype/features/_encoded.py

@@ -39,6 +39,9 @@ def image_size(self) -> Tuple[int, int]:
         return self._image_size
 
     def decode(self) -> Image:
+        # TODO: this is useful for developing and debugging but we should remove or at least revisit this before we
+        #  promote this out of the prototype state
+
         # import at runtime to avoid cyclic imports
         from torchvision.prototype.transforms.kernels import decode_image_with_pil
 

torchvision/prototype/features/_feature.py

@@ -12,20 +12,20 @@ class Feature(torch.Tensor):
     _metadata: Dict[str, Any]
 
     def __init_subclass__(cls) -> None:
-        # In order to help static type checkers, we require subclasses of `Feature` to add the metadata attributes
-        # as static class annotations:
-        #
-        # >>> class Foo(Feature):
-        # ...     bar: str
-        # ...     baz: Optional[str]
-        #
-        # Internally, this information is used twofold:
-        #
-        # 1. A class annotation is contained in `cls.__annotations__` but not in `cls.__dict__`. We use this difference
-        #    to automatically detect the meta data attributes and expose them as `@property`'s for convenient runtime
-        #    access. This happens in this method.
-        # 2. The information extracted in 1. is also used at creation (`__new__`) to perform an input parsing for
-        #    unknown arguments.
+        """
+        For convenient copying of metadata, we store it inside a dictionary rather than multiple individual attributes.
+        By adding the metadata attributes as class annotations on subclasses of :class:`Feature`, this method adds
+        properties to have the same convenient access as regular attributes.
+
+        >>> class Foo(Feature):
+        ...     bar: str
+        ...     baz: Optional[str]
+        >>> foo = Foo()
+        >>> foo.bar
+        >>> foo.baz
+
+        This has the additional benefit that autocomplete engines and static type checkers are aware of the metadata.
+        """
         meta_attrs = {attr for attr in cls.__annotations__.keys() - cls.__dict__.keys() if not attr.startswith("_")}
         for super_cls in cls.__mro__[1:]:
             if super_cls is Feature:

torchvision/prototype/features/_image.py

@@ -78,7 +78,11 @@ def guess_color_space(data: torch.Tensor) -> ColorSpace:
             return ColorSpace.OTHER
 
     def show(self) -> None:
+        # TODO: this is useful for developing and debugging but we should remove or at least revisit this before we
+        #  promote this out of the prototype state
         to_pil_image(make_grid(self.view(-1, *self.shape[-3:]))).show()
 
     def draw_bounding_box(self, bounding_box: BoundingBox, **kwargs: Any) -> Image:
+        # TODO: this is useful for developing and debugging but we should remove or at least revisit this before we
+        #  promote this out of the prototype state
         return Image.new_like(self, draw_bounding_boxes(self, bounding_box.to_format("xyxy").view(-1, 4), **kwargs))

torchvision/prototype/transforms/functional/_geometry.py

@@ -41,7 +41,7 @@ def resize(input: T, *args: Any, **kwargs: Any) -> T:
     """ADDME"""
     if isinstance(input, features.BoundingBox):
         size = kwargs.pop("size")
-        output = K.resize_bounding_box(input, old_image_size=list(input.image_size), new_image_size=size)
+        output = K.resize_bounding_box(input, size=size, image_size=input.image_size)
         return cast(T, features.BoundingBox.new_like(input, output, image_size=size))
 
     raise RuntimeError

torchvision/prototype/transforms/kernels/_geometry.py

@@ -51,21 +51,15 @@ def resize_image(
 def resize_segmentation_mask(
     segmentation_mask: torch.Tensor,
     size: List[int],
-    interpolation: InterpolationMode = InterpolationMode.NEAREST,
     max_size: Optional[int] = None,
-    antialias: Optional[bool] = None,
 ) -> torch.Tensor:
-    return resize_image(
-        segmentation_mask, size=size, interpolation=interpolation, max_size=max_size, antialias=antialias
-    )
+    return resize_image(segmentation_mask, size=size, interpolation=InterpolationMode.NEAREST, max_size=max_size)
 
 
 # TODO: handle max_size
-def resize_bounding_box(
-    bounding_box: torch.Tensor, *, old_image_size: List[int], new_image_size: List[int]
-) -> torch.Tensor:
-    old_height, old_width = old_image_size
-    new_height, new_width = new_image_size
+def resize_bounding_box(bounding_box: torch.Tensor, *, size: List[int], image_size: Tuple[int, int]) -> torch.Tensor:
+    old_height, old_width = image_size
+    new_height, new_width = size
     ratios = torch.tensor((new_width / old_width, new_height / old_height), device=bounding_box.device)
     return bounding_box.view(-1, 2, 2).mul(ratios).view(bounding_box.shape)