[proto] Use F.pad in RandomCrop (#6392)

* [proto] Use F.pad in RandomCrop

* Update torchvision/prototype/transforms/_geometry.py

torchvision/prototype/transforms/_geometry.py

@@ -434,18 +434,17 @@ class RandomCrop(Transform):
 
         self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
 
+        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.padding_mode = padding_mode
 
-        self._pad_op = None
-        if self.padding is not None:
-            self._pad_op = Pad(self.padding, fill=self.fill, padding_mode=self.padding_mode)
-
-        if self.pad_if_needed:
-            self._pad_op = Pad(0, fill=self.fill, padding_mode=self.padding_mode)
-
     def _get_params(self, sample: Any) -> Dict[str, Any]:
         image = query_image(sample)
         _, height, width = get_image_dimensions(image)
@@ -469,28 +468,21 @@ class RandomCrop(Transform):
     def forward(self, *inputs: Any) -> Any:
         sample = inputs if len(inputs) > 1 else inputs[0]
 
-        if self._pad_op is not None:
-            sample = self._pad_op(sample)
+        if self.padding is not None:
+            sample = F.pad(sample, padding=self.padding, fill=self.fill, padding_mode=self.padding_mode)
 
         image = query_image(sample)
         _, height, width = get_image_dimensions(image)
 
         if self.pad_if_needed:
-            # This check is to explicitly ensure that self._pad_op is defined
-            if self._pad_op is None:
-                raise RuntimeError(
-                    "Internal error, self._pad_op is None. "
-                    "Please, fill an issue about that on https://github.com/pytorch/vision/issues"
-                )
-
             # pad the width if needed
             if width < self.size[1]:
-                self._pad_op.padding = [self.size[1] - width, 0]
-                sample = self._pad_op(sample)
+                padding = [self.size[1] - width, 0]
+                sample = F.pad(sample, padding=padding, fill=self.fill, padding_mode=self.padding_mode)
             # pad the height if needed
             if height < self.size[0]:
-                self._pad_op.padding = [0, self.size[0] - height]
-                sample = self._pad_op(sample)
+                padding = [0, self.size[0] - height]
+                sample = F.pad(sample, padding=padding, fill=self.fill, padding_mode=self.padding_mode)
 
         return super().forward(sample)