[proto] Added dict support for fill arg for remaining transforms (#6599)

* Updated fill arg typehint for affine, perspective and elastic ops

* Updated pad op on prototype side

* Code updates

* Few other minor updates

* WIP

* WIP

* Updates

* Update _image.py

* Fixed tests

test/test_prototype_transforms.py

@@ -391,7 +391,7 @@ class TestPad:
         if isinstance(fill, int):
             calls = [
                 mocker.call(image, padding=1, fill=fill, padding_mode="constant"),
-                mocker.call(mask, padding=1, fill=0, padding_mode="constant"),
+                mocker.call(mask, padding=1, fill=fill, padding_mode="constant"),
             ]
         else:
             calls = [
@@ -467,7 +467,7 @@ class TestRandomZoomOut:
         if isinstance(fill, int):
             calls = [
                 mocker.call(image, **params, fill=fill),
-                mocker.call(mask, **params, fill=0),
+                mocker.call(mask, **params, fill=fill),
             ]
         else:
             calls = [
@@ -1555,7 +1555,7 @@ class TestFixedSizeCrop:
 
     @pytest.mark.parametrize("needs", list(itertools.product((False, True), repeat=2)))
     def test__transform(self, mocker, needs):
-        fill_sentinel = mocker.MagicMock()
+        fill_sentinel = 12
         padding_mode_sentinel = mocker.MagicMock()
 
         transform = transforms.FixedSizeCrop((-1, -1), fill=fill_sentinel, padding_mode=padding_mode_sentinel)

test/test_prototype_transforms_functional.py

@@ -195,9 +195,16 @@ def pad_image_tensor():
     for image, padding, fill, padding_mode in itertools.product(
         make_images(),
         [[1], [1, 1], [1, 1, 2, 2]],  # padding
-        [None, 12, 12.0],  # fill
+        [None, 128.0, 128, [12.0], [12.0, 13.0, 14.0]],  # fill
         ["constant", "symmetric", "edge", "reflect"],  # padding mode,
     ):
+        if padding_mode != "constant" and fill is not None:
+            # ValueError: Padding mode 'reflect' is not supported if fill is not scalar
+            continue
+
+        if isinstance(fill, list) and len(fill) != image.shape[-3]:
+            continue
+
         yield ArgsKwargs(image, padding=padding, fill=fill, padding_mode=padding_mode)
 
 

torchvision/prototype/transforms/_geometry.py

@@ -211,10 +211,12 @@ def _check_fill_arg(fill: Union[FillType, Dict[Type, FillType]]) -> None:
 
 
 def _setup_fill_arg(fill: Union[FillType, Dict[Type, FillType]]) -> Dict[Type, FillType]:
+    _check_fill_arg(fill)
+
     if isinstance(fill, dict):
         return fill
-    else:
-        return defaultdict(lambda: fill, {features.Mask: 0})  # type: ignore[arg-type, return-value]
+
+    return defaultdict(lambda: fill)  # type: ignore[arg-type, return-value]
 
 
 def _check_padding_arg(padding: Union[int, Sequence[int]]) -> None:
@@ -242,7 +244,6 @@ class Pad(Transform):
         super().__init__()
 
         _check_padding_arg(padding)
-        _check_fill_arg(fill)
         _check_padding_mode_arg(padding_mode)
 
         self.padding = padding
@@ -263,7 +264,6 @@ class RandomZoomOut(_RandomApplyTransform):
     ) -> None:
         super().__init__(p=p)
 
-        _check_fill_arg(fill)
         self.fill = _setup_fill_arg(fill)
 
         _check_sequence_input(side_range, "side_range", req_sizes=(2,))
@@ -299,7 +299,7 @@ class RandomRotation(Transform):
         degrees: Union[numbers.Number, Sequence],
         interpolation: InterpolationMode = InterpolationMode.NEAREST,
         expand: bool = False,
-        fill: Union[int, float, Sequence[int], Sequence[float]] = 0,
+        fill: Union[FillType, Dict[Type, FillType]] = 0,
         center: Optional[List[float]] = None,
     ) -> None:
         super().__init__()
@@ -307,9 +307,7 @@ class RandomRotation(Transform):
         self.interpolation = interpolation
         self.expand = expand
 
-        _check_fill_arg(fill)
-
-        self.fill = fill
+        self.fill = _setup_fill_arg(fill)
 
         if center is not None:
             _check_sequence_input(center, "center", req_sizes=(2,))
@@ -321,12 +319,13 @@ class RandomRotation(Transform):
         return dict(angle=angle)
 
     def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+        fill = self.fill[type(inpt)]
         return F.rotate(
             inpt,
             **params,
             interpolation=self.interpolation,
             expand=self.expand,
-            fill=self.fill,
+            fill=fill,
             center=self.center,
         )
 
@@ -339,7 +338,7 @@ class RandomAffine(Transform):
         scale: Optional[Sequence[float]] = None,
         shear: Optional[Union[float, Sequence[float]]] = None,
         interpolation: InterpolationMode = InterpolationMode.NEAREST,
-        fill: Union[int, float, Sequence[int], Sequence[float]] = 0,
+        fill: Union[FillType, Dict[Type, FillType]] = 0,
         center: Optional[List[float]] = None,
     ) -> None:
         super().__init__()
@@ -363,10 +362,7 @@ class RandomAffine(Transform):
             self.shear = shear
 
         self.interpolation = interpolation
-
-        _check_fill_arg(fill)
-
-        self.fill = fill
+        self.fill = _setup_fill_arg(fill)
 
         if center is not None:
             _check_sequence_input(center, "center", req_sizes=(2,))
@@ -404,11 +400,12 @@ class RandomAffine(Transform):
         return dict(angle=angle, translate=translate, scale=scale, shear=shear)
 
     def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+        fill = self.fill[type(inpt)]
         return F.affine(
             inpt,
             **params,
             interpolation=self.interpolation,
-            fill=self.fill,
+            fill=fill,
             center=self.center,
         )
 
@@ -419,7 +416,7 @@ class RandomCrop(Transform):
         size: Union[int, Sequence[int]],
         padding: Optional[Union[int, Sequence[int]]] = None,
         pad_if_needed: bool = False,
-        fill: Union[int, float, Sequence[int], Sequence[float]] = 0,
+        fill: Union[FillType, Dict[Type, FillType]] = 0,
         padding_mode: Literal["constant", "edge", "reflect", "symmetric"] = "constant",
     ) -> None:
         super().__init__()
@@ -429,12 +426,11 @@ class RandomCrop(Transform):
         if pad_if_needed or padding is not None:
             if padding is not None:
                 _check_padding_arg(padding)
-            _check_fill_arg(fill)
             _check_padding_mode_arg(padding_mode)
 
         self.padding = padding
         self.pad_if_needed = pad_if_needed
-        self.fill = fill
+        self.fill = _setup_fill_arg(fill)
         self.padding_mode = padding_mode
 
     def _get_params(self, sample: Any) -> Dict[str, Any]:
@@ -483,17 +479,18 @@ class RandomCrop(Transform):
 
     def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
         # TODO: (PERF) check for speed optimization if we avoid repeated pad calls
+        fill = self.fill[type(inpt)]
         if self.padding is not None:
-            inpt = F.pad(inpt, padding=self.padding, fill=self.fill, padding_mode=self.padding_mode)
+            inpt = F.pad(inpt, padding=self.padding, fill=fill, padding_mode=self.padding_mode)
 
         if self.pad_if_needed:
             input_width, input_height = params["input_width"], params["input_height"]
             if input_width < self.size[1]:
                 padding = [self.size[1] - input_width, 0]
-                inpt = F.pad(inpt, padding=padding, fill=self.fill, padding_mode=self.padding_mode)
+                inpt = F.pad(inpt, padding=padding, fill=fill, padding_mode=self.padding_mode)
             if input_height < self.size[0]:
                 padding = [0, self.size[0] - input_height]
-                inpt = F.pad(inpt, padding=padding, fill=self.fill, padding_mode=self.padding_mode)
+                inpt = F.pad(inpt, padding=padding, fill=fill, padding_mode=self.padding_mode)
 
         return F.crop(inpt, top=params["top"], left=params["left"], height=params["height"], width=params["width"])
 
@@ -502,19 +499,18 @@ class RandomPerspective(_RandomApplyTransform):
     def __init__(
         self,
         distortion_scale: float = 0.5,
-        fill: Union[int, float, Sequence[int], Sequence[float]] = 0,
+        fill: Union[FillType, Dict[Type, FillType]] = 0,
         interpolation: InterpolationMode = InterpolationMode.BILINEAR,
         p: float = 0.5,
     ) -> None:
         super().__init__(p=p)
 
-        _check_fill_arg(fill)
         if not (0 <= distortion_scale <= 1):
             raise ValueError("Argument distortion_scale value should be between 0 and 1")
 
         self.distortion_scale = distortion_scale
         self.interpolation = interpolation
-        self.fill = fill
+        self.fill = _setup_fill_arg(fill)
 
     def _get_params(self, sample: Any) -> Dict[str, Any]:
         # Get image size
@@ -546,10 +542,11 @@ class RandomPerspective(_RandomApplyTransform):
         return dict(startpoints=startpoints, endpoints=endpoints)
 
     def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+        fill = self.fill[type(inpt)]
         return F.perspective(
             inpt,
             **params,
-            fill=self.fill,
+            fill=fill,
             interpolation=self.interpolation,
         )
 
@@ -576,17 +573,15 @@ class ElasticTransform(Transform):
         self,
         alpha: Union[float, Sequence[float]] = 50.0,
         sigma: Union[float, Sequence[float]] = 5.0,
-        fill: Union[int, float, Sequence[int], Sequence[float]] = 0,
+        fill: Union[FillType, Dict[Type, FillType]] = 0,
         interpolation: InterpolationMode = InterpolationMode.BILINEAR,
     ) -> None:
         super().__init__()
         self.alpha = _setup_float_or_seq(alpha, "alpha", 2)
         self.sigma = _setup_float_or_seq(sigma, "sigma", 2)
 
-        _check_fill_arg(fill)
-
         self.interpolation = interpolation
-        self.fill = fill
+        self.fill = _setup_fill_arg(fill)
 
     def _get_params(self, sample: Any) -> Dict[str, Any]:
         # Get image size
@@ -614,10 +609,11 @@ class ElasticTransform(Transform):
         return dict(displacement=displacement)
 
     def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+        fill = self.fill[type(inpt)]
         return F.elastic(
             inpt,
             **params,
-            fill=self.fill,
+            fill=fill,
             interpolation=self.interpolation,
         )
 
@@ -789,14 +785,16 @@ class FixedSizeCrop(Transform):
     def __init__(
         self,
         size: Union[int, Sequence[int]],
-        fill: Union[int, float, Sequence[int], Sequence[float]] = 0,
+        fill: Union[FillType, Dict[Type, FillType]] = 0,
         padding_mode: str = "constant",
     ) -> None:
         super().__init__()
         size = tuple(_setup_size(size, error_msg="Please provide only two dimensions (h, w) for size."))
         self.crop_height = size[0]
         self.crop_width = size[1]
-        self.fill = fill  # TODO: Fill is currently respected only on PIL. Apply tensor patch.
+
+        self.fill = _setup_fill_arg(fill)
+
         self.padding_mode = padding_mode
 
     def _get_params(self, sample: Any) -> Dict[str, Any]:
@@ -869,7 +867,8 @@ class FixedSizeCrop(Transform):
                 )
 
         if params["needs_pad"]:
-            inpt = F.pad(inpt, params["padding"], fill=self.fill, padding_mode=self.padding_mode)
+            fill = self.fill[type(inpt)]
+            inpt = F.pad(inpt, params["padding"], fill=fill, padding_mode=self.padding_mode)
 
         return inpt
 

torchvision/transforms/functional_tensor.py

@@ -499,7 +499,7 @@ def _assert_grid_transform_inputs(
 
     # Check fill
     num_channels = get_dimensions(img)[0]
-    if fill is not None and isinstance(fill, (tuple, list)) and (len(fill) > 1 and len(fill) != num_channels):
+    if fill is not None and isinstance(fill, (tuple, list)) and len(fill) > 1 and len(fill) != num_channels:
         msg = (
             "The number of elements in 'fill' cannot broadcast to match the number of "
             "channels of the image ({} != {})"