[fbsync] Let LinearTransformation return datapoints instead of tensors (#7244)

Reviewed By: vmoens

Differential Revision: D44416585

fbshipit-source-id: 8f61172e676563ad7eefbdb294eb76dab6dee2eb

Author: NicolasHug

torchvision/prototype/transforms/_misc.py

@@ -76,12 +76,7 @@ def _check_inputs(self, sample: Any) -> Any:
         if has_any(sample, PIL.Image.Image):
             raise TypeError("LinearTransformation does not work on PIL Images")
 
-    def _transform(
-        self, inpt: Union[datapoints.TensorImageType, datapoints.TensorVideoType], params: Dict[str, Any]
-    ) -> torch.Tensor:
-        # Image instance after linear transformation is not Image anymore due to unknown data range
-        # Thus we will return Tensor for input Image
-
+    def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
         shape = inpt.shape
         n = shape[-3] * shape[-2] * shape[-1]
         if n != self.transformation_matrix.shape[0]:
@@ -97,11 +92,15 @@ def _transform(
                 f"Got {inpt.device} vs {self.mean_vector.device}"
             )
 
-        flat_tensor = inpt.reshape(-1, n) - self.mean_vector
+        flat_inpt = inpt.reshape(-1, n) - self.mean_vector
+
+        transformation_matrix = self.transformation_matrix.to(flat_inpt.dtype)
+        output = torch.mm(flat_inpt, transformation_matrix)
+        output = output.reshape(shape)
 
-        transformation_matrix = self.transformation_matrix.to(flat_tensor.dtype)
-        transformed_tensor = torch.mm(flat_tensor, transformation_matrix)
-        return transformed_tensor.reshape(shape)
+        if isinstance(inpt, (datapoints.Image, datapoints.Video)):
+            output = type(inpt).wrap_like(inpt, output)  # type: ignore[arg-type]
+        return output
 
 
 class Normalize(Transform):
@@ -120,7 +119,7 @@ def _check_inputs(self, sample: Any) -> Any:
 
     def _transform(
         self, inpt: Union[datapoints.TensorImageType, datapoints.TensorVideoType], params: Dict[str, Any]
-    ) -> torch.Tensor:
+    ) -> Any:
         return F.normalize(inpt, mean=self.mean, std=self.std, inplace=self.inplace)