[proto] Added erase_image_pil and eager/jit erase_image_tensor test

test/test_prototype_transforms_functional.py

@@ -654,6 +654,13 @@ def adjust_sharpness_image_tensor():
         yield SampleInput(image, sharpness_factor=sharpness_factor)
 
 
+@register_kernel_info_from_sample_inputs_fn
+def erase_image_tensor():
+    for image in make_images():
+        c = image.shape[-3]
+        yield SampleInput(image, i=1, j=2, h=6, w=7, v=torch.rand(c, 6, 7))
+
+
 @pytest.mark.parametrize(
     "kernel",
     [

torchvision/prototype/transforms/_augment.py

@@ -7,7 +7,6 @@
 import torch
 from torchvision.prototype import features
 from torchvision.prototype.transforms import functional as F, Transform
-from torchvision.transforms.functional import pil_to_tensor, to_pil_image
 
 from ._transform import _RandomApplyTransform
 from ._utils import get_image_dimensions, has_all, has_any, query_image
@@ -93,9 +92,7 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
                 return features.Image.new_like(inpt, output)
             return output
         elif isinstance(inpt, PIL.Image.Image):
-            t_img = pil_to_tensor(inpt)
-            output = F.erase_image_tensor(t_img, **params)
-            return to_pil_image(output, mode=inpt.mode)
+            return F.erase_image_pil(inpt, **params)
         else:
             return inpt
 

torchvision/prototype/transforms/functional/__init__.py

@@ -5,7 +5,7 @@
     convert_image_color_space_pil,
 )  # usort: skip
 
-from ._augment import erase_image_tensor
+from ._augment import erase_image_pil, erase_image_tensor
 from ._color import (
     adjust_brightness,
     adjust_brightness_image_pil,

torchvision/prototype/transforms/functional/_augment.py

@@ -1,13 +1,16 @@
+import PIL.Image
+
+import torch
 from torchvision.transforms import functional_tensor as _FT
+from torchvision.transforms.functional import pil_to_tensor, to_pil_image
 
 
 erase_image_tensor = _FT.erase
 
 
-# TODO: Don't forget to clean up from the primitives kernels those that shouldn't be kernels.
-# Like the mixup and cutmix stuff
-
-# This function is copy-pasted to Image and OneHotLabel and may be refactored
-# def _mixup_tensor(input: torch.Tensor, batch_dim: int, lam: float) -> torch.Tensor:
-#     input = input.clone()
-#     return input.roll(1, batch_dim).mul_(1 - lam).add_(input.mul_(lam))
+def erase_image_pil(
+    img: PIL.Image.Image, i: int, j: int, h: int, w: int, v: torch.Tensor, inplace: bool = False
+) -> PIL.Image.Image:
+    t_img = pil_to_tensor(img)
+    output = erase_image_tensor(t_img, i=i, j=j, h=h, w=w, v=v, inplace=inplace)
+    return to_pil_image(output, mode=img.mode)