make fill defaultdict an implementation detail (#7258)

torchvision/prototype/transforms/_geometry.py

@@ -22,7 +22,8 @@ class FixedSizeCrop(Transform):
         self.crop_height = size[0]
         self.crop_width = size[1]
 
-        self.fill = _setup_fill_arg(fill)
+        self.fill = fill
+        self._fill = _setup_fill_arg(fill)
 
         self.padding_mode = padding_mode
 
@@ -118,7 +119,7 @@ class FixedSizeCrop(Transform):
                 )
 
         if params["needs_pad"]:
-            fill = self.fill[type(inpt)]
+            fill = self._fill[type(inpt)]
             inpt = F.pad(inpt, params["padding"], fill=fill, padding_mode=self.padding_mode)
 
         return inpt

torchvision/transforms/v2/_geometry.py

@@ -255,9 +255,7 @@ class Pad(Transform):
         params = super()._extract_params_for_v1_transform()
 
         if not (params["fill"] is None or isinstance(params["fill"], (int, float))):
-            raise ValueError(
-                f"{type(self.__name__)}() can only be scripted for a scalar `fill`, but got {self.fill} for images."
-            )
+            raise ValueError(f"{type(self).__name__}() can only be scripted for a scalar `fill`, but got {self.fill}.")
 
         return params
 
@@ -276,11 +274,12 @@ class Pad(Transform):
         if not isinstance(padding, int):
             padding = list(padding)
         self.padding = padding
-        self.fill = _setup_fill_arg(fill)
+        self.fill = fill
+        self._fill = _setup_fill_arg(fill)
         self.padding_mode = padding_mode
 
     def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
-        fill = self.fill[type(inpt)]
+        fill = self._fill[type(inpt)]
         return F.pad(inpt, padding=self.padding, fill=fill, padding_mode=self.padding_mode)  # type: ignore[arg-type]
 
 
@@ -293,7 +292,8 @@ class RandomZoomOut(_RandomApplyTransform):
     ) -> None:
         super().__init__(p=p)
 
-        self.fill = _setup_fill_arg(fill)
+        self.fill = fill
+        self._fill = _setup_fill_arg(fill)
 
         _check_sequence_input(side_range, "side_range", req_sizes=(2,))
 
@@ -318,7 +318,7 @@ class RandomZoomOut(_RandomApplyTransform):
         return dict(padding=padding)
 
     def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
-        fill = self.fill[type(inpt)]
+        fill = self._fill[type(inpt)]
         return F.pad(inpt, **params, fill=fill)
 
 
@@ -338,7 +338,8 @@ class RandomRotation(Transform):
         self.interpolation = _check_interpolation(interpolation)
         self.expand = expand
 
-        self.fill = _setup_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,))
@@ -350,7 +351,7 @@ class RandomRotation(Transform):
         return dict(angle=angle)
 
     def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
-        fill = self.fill[type(inpt)]
+        fill = self._fill[type(inpt)]
         return F.rotate(
             inpt,
             **params,
@@ -395,7 +396,8 @@ class RandomAffine(Transform):
             self.shear = shear
 
         self.interpolation = _check_interpolation(interpolation)
-        self.fill = _setup_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,))
@@ -430,7 +432,7 @@ 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)]
+        fill = self._fill[type(inpt)]
         return F.affine(
             inpt,
             **params,
@@ -447,9 +449,7 @@ class RandomCrop(Transform):
         params = super()._extract_params_for_v1_transform()
 
         if not (params["fill"] is None or isinstance(params["fill"], (int, float))):
-            raise ValueError(
-                f"{type(self.__name__)}() can only be scripted for a scalar `fill`, but got {self.fill} for images."
-            )
+            raise ValueError(f"{type(self).__name__}() can only be scripted for a scalar `fill`, but got {self.fill}.")
 
         padding = self.padding
         if padding is not None:
@@ -478,7 +478,8 @@ class RandomCrop(Transform):
 
         self.padding = F._geometry._parse_pad_padding(padding) if padding else None  # type: ignore[arg-type]
         self.pad_if_needed = pad_if_needed
-        self.fill = _setup_fill_arg(fill)
+        self.fill = fill
+        self._fill = _setup_fill_arg(fill)
         self.padding_mode = padding_mode
 
     def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
@@ -541,7 +542,7 @@ class RandomCrop(Transform):
 
     def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
         if params["needs_pad"]:
-            fill = self.fill[type(inpt)]
+            fill = self._fill[type(inpt)]
             inpt = F.pad(inpt, padding=params["padding"], fill=fill, padding_mode=self.padding_mode)
 
         if params["needs_crop"]:
@@ -567,7 +568,8 @@ class RandomPerspective(_RandomApplyTransform):
 
         self.distortion_scale = distortion_scale
         self.interpolation = _check_interpolation(interpolation)
-        self.fill = _setup_fill_arg(fill)
+        self.fill = fill
+        self._fill = _setup_fill_arg(fill)
 
     def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
         height, width = query_spatial_size(flat_inputs)
@@ -600,7 +602,7 @@ class RandomPerspective(_RandomApplyTransform):
         return dict(coefficients=perspective_coeffs)
 
     def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
-        fill = self.fill[type(inpt)]
+        fill = self._fill[type(inpt)]
         return F.perspective(
             inpt,
             None,
@@ -626,7 +628,8 @@ class ElasticTransform(Transform):
         self.sigma = _setup_float_or_seq(sigma, "sigma", 2)
 
         self.interpolation = _check_interpolation(interpolation)
-        self.fill = _setup_fill_arg(fill)
+        self.fill = fill
+        self._fill = _setup_fill_arg(fill)
 
     def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
         size = list(query_spatial_size(flat_inputs))
@@ -652,7 +655,7 @@ class ElasticTransform(Transform):
         return dict(displacement=displacement)
 
     def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
-        fill = self.fill[type(inpt)]
+        fill = self._fill[type(inpt)]
         return F.elastic(
             inpt,
             **params,

torchvision/transforms/v2/_transform.py

@@ -108,30 +108,17 @@ class Transform(nn.Module):
 
     def _extract_params_for_v1_transform(self) -> Dict[str, Any]:
         # This method is called by `__prepare_scriptable__` to instantiate the equivalent v1 transform from the current
-        # v2 transform instance. It does two things:
-        # 1. Extract all available public attributes that are specific to that transform and not `nn.Module` in general
-        # 2. If available handle the `fill` attribute for v1 compatibility (see below for details)
+        # v2 transform instance. It extracts all available public attributes that are specific to that transform and
+        # not `nn.Module` in general.
         # Overwrite this method on the v2 transform class if the above is not sufficient. For example, this might happen
         # if the v2 transform introduced new parameters that are not support by the v1 transform.
         common_attrs = nn.Module().__dict__.keys()
-        params = {
+        return {
             attr: value
             for attr, value in self.__dict__.items()
             if not attr.startswith("_") and attr not in common_attrs
         }
 
-        # transforms v2 has a more complex handling for the `fill` parameter than v1. By default, the input is parsed
-        # with `prototype.transforms._utils._setup_fill_arg()`, which returns a defaultdict that holds the fill value
-        # for the different datapoint types. Below we extract the value for tensors and return that together with the
-        # other params.
-        # This is needed for `Pad`, `ElasticTransform`, `RandomAffine`, `RandomCrop`, `RandomPerspective` and
-        # `RandomRotation`
-        if "fill" in params:
-            fill_type_defaultdict = params.pop("fill")
-            params["fill"] = fill_type_defaultdict[torch.Tensor]
-
-        return params
-
     def __prepare_scriptable__(self) -> nn.Module:
         # This method is called early on when `torch.jit.script`'ing an `nn.Module` instance. If it succeeds, the return
         # value is used for scripting over the original object that should have been scripted. Since the v1 transforms