[prototype] Speed up Augment Transform Classes (#6835)

* Moving value estimation of `RandomErasing` from runtime to constructor.

* Speed up mixing on MixUp/Cutmix and small optimization on SimpleCopyPaste.

* Apply nits.

Author: datumbox

torchvision/prototype/transforms/_augment.py

@@ -40,24 +40,22 @@ def __init__(
             raise ValueError("Scale should be between 0 and 1")
         self.scale = scale
         self.ratio = ratio
-        self.value = value
+        if isinstance(value, (int, float)):
+            self.value = [value]
+        elif isinstance(value, str):
+            self.value = None
+        elif isinstance(value, tuple):
+            self.value = list(value)
+        else:
+            self.value = value
         self.inplace = inplace
 
         self._log_ratio = torch.log(torch.tensor(self.ratio))
 
     def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
         img_c, img_h, img_w = query_chw(flat_inputs)
 
-        if isinstance(self.value, (int, float)):
-            value = [self.value]
-        elif isinstance(self.value, str):
-            value = None
-        elif isinstance(self.value, tuple):
-            value = list(self.value)
-        else:
-            value = self.value
-
-        if value is not None and not (len(value) in (1, img_c)):
+        if self.value is not None and not (len(self.value) in (1, img_c)):
             raise ValueError(
                 f"If value is a sequence, it should have either a single value or {img_c} (number of inpt channels)"
             )
@@ -79,10 +77,10 @@ def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
             if not (h < img_h and w < img_w):
                 continue
 
-            if value is None:
+            if self.value is None:
                 v = torch.empty([img_c, h, w], dtype=torch.float32).normal_()
             else:
-                v = torch.tensor(value)[:, None, None]
+                v = torch.tensor(self.value)[:, None, None]
 
             i = torch.randint(0, img_h - h + 1, size=(1,)).item()
             j = torch.randint(0, img_w - w + 1, size=(1,)).item()
@@ -121,8 +119,7 @@ def _check_inputs(self, flat_inputs: List[Any]) -> None:
     def _mixup_onehotlabel(self, inpt: features.OneHotLabel, lam: float) -> features.OneHotLabel:
         if inpt.ndim < 2:
             raise ValueError("Need a batch of one hot labels")
-        output = inpt.clone()
-        output = output.roll(1, 0).mul_(1.0 - lam).add_(output.mul_(lam))
+        output = inpt.roll(1, 0).mul_(1.0 - lam).add_(inpt.mul(lam))
         return features.OneHotLabel.wrap_like(inpt, output)
 
 
@@ -136,8 +133,7 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
             expected_ndim = 5 if isinstance(inpt, features.Video) else 4
             if inpt.ndim < expected_ndim:
                 raise ValueError("The transform expects a batched input")
-            output = inpt.clone()
-            output = output.roll(1, 0).mul_(1.0 - lam).add_(output.mul_(lam))
+            output = inpt.roll(1, 0).mul_(1.0 - lam).add_(inpt.mul(lam))
 
             if isinstance(inpt, (features.Image, features.Video)):
                 output = type(inpt).wrap_like(inpt, output)  # type: ignore[arg-type]
@@ -243,11 +239,12 @@ def _copy_paste(
         if blending:
             paste_alpha_mask = F.gaussian_blur(paste_alpha_mask.unsqueeze(0), kernel_size=[5, 5], sigma=[2.0])
 
+        inverse_paste_alpha_mask = paste_alpha_mask.logical_not()
         # Copy-paste images:
-        image = (image * (~paste_alpha_mask)) + (paste_image * paste_alpha_mask)
+        image = image.mul(inverse_paste_alpha_mask).add_(paste_image.mul(paste_alpha_mask))
 
         # Copy-paste masks:
-        masks = masks * (~paste_alpha_mask)
+        masks = masks * inverse_paste_alpha_mask
         non_all_zero_masks = masks.sum((-1, -2)) > 0
         masks = masks[non_all_zero_masks]