Update transforms for PIL deprecation

test/test_onnx.py

@@ -413,11 +413,12 @@ def get_image(self, rel_path: str, size: Tuple[int, int]) -> torch.Tensor:
         import os
 
         from PIL import Image
+        import torchvision.transforms._pil_constants as _pil_constants
         from torchvision.transforms import functional as F
 
         data_dir = os.path.join(os.path.dirname(__file__), "assets")
         path = os.path.join(data_dir, *rel_path.split("/"))
-        image = Image.open(path).convert("RGB").resize(size, Image.BILINEAR)
+        image = Image.open(path).convert("RGB").resize(size, _pil_constants.BILINEAR)
 
         return F.convert_image_dtype(F.pil_to_tensor(image))
 

test/test_transforms.py

@@ -12,6 +12,7 @@
 import torchvision.transforms.functional_tensor as F_t
 from PIL import Image
 from torch._utils_internal import get_file_path_2
+import torchvision.transforms._pil_constants as _pil_constants
 
 try:
     import accimage
@@ -173,7 +174,7 @@ def test_accimage_pil_to_tensor(self):
     def test_accimage_resize(self):
         trans = transforms.Compose(
             [
-                transforms.Resize(256, interpolation=Image.LINEAR),
+                transforms.Resize(256, interpolation=_pil_constants.LINEAR),
                 transforms.PILToTensor(),
                 transforms.ConvertImageDtype(dtype=torch.float),
             ]

test/test_transforms_tensor.py

@@ -15,11 +15,12 @@
     cpu_and_gpu,
     assert_equal,
 )
-from PIL import Image
+
 from torchvision import transforms as T
 from torchvision.transforms import InterpolationMode
 from torchvision.transforms import functional as F
 from torchvision.transforms.autoaugment import _apply_op
+import torchvision.transforms._pil_constants as _pil_constants
 
 NEAREST, BILINEAR, BICUBIC = InterpolationMode.NEAREST, InterpolationMode.BILINEAR, InterpolationMode.BICUBIC
 
@@ -771,13 +772,13 @@ def shear(pil_img, level, mode, resample):
             matrix = (1, level, 0, 0, 1, 0)
         elif mode == "Y":
             matrix = (1, 0, 0, level, 1, 0)
-        return pil_img.transform((image_size, image_size), Image.AFFINE, matrix, resample=resample)
+        return pil_img.transform((image_size, image_size), _pil_constants.AFFINE, matrix, resample=resample)
 
     t_img, pil_img = _create_data(image_size, image_size)
 
     resample_pil = {
-        F.InterpolationMode.NEAREST: Image.NEAREST,
-        F.InterpolationMode.BILINEAR: Image.BILINEAR,
+        F.InterpolationMode.NEAREST: _pil_constants.NEAREST,
+        F.InterpolationMode.BILINEAR: _pil_constants.BILINEAR,
     }[interpolation]
 
     level = 0.3

torchvision/transforms/_pil_constants.py

@@ -0,0 +1,22 @@
+
+import PIL
+from PIL import Image
+
+if tuple(int(part) for part in PIL.__version__.split(".")) >= (9, 1, 0):
+    FLIP_LEFT_RIGHT = Image.Transpose.FLIP_LEFT_RIGHT
+    FLIP_TOP_BOTTOM = Image.Transpose.FLIP_TOP_BOTTOM
+    BILINEAR = Image.Resampling.BILINEAR
+    NEAREST = Image.Resampling.NEAREST
+    AFFINE = Image.Transform.AFFINE
+    BICUBIC = Image.Resampling.BICUBIC
+    PERSPECTIVE = Image.Transform.PERSPECTIVE
+    LINEAR = Image.Resampling.BILINEAR
+else:
+    FLIP_LEFT_RIGHT = Image.FLIP_LEFT_RIGHT
+    FLIP_TOP_BOTTOM = Image.FLIP_TOP_BOTTOM
+    BILINEAR = Image.BILINEAR
+    NEAREST = Image.NEAREST
+    AFFINE = Image.AFFINE
+    BICUBIC = Image.BICUBIC
+    PERSPECTIVE = Image.PERSPECTIVE
+    LINEAR = Image.LINEAR

torchvision/transforms/functional.py

@@ -392,7 +392,7 @@ def resize(
             :class:`torchvision.transforms.InterpolationMode`.
             Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``,
             ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
-            For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
+            For backward compatibility integer values (e.g. ``PIL.Image(.Resampling).NEAREST``) are still acceptable.
         max_size (int, optional): The maximum allowed for the longer edge of
             the resized image: if the longer edge of the image is greater
             than ``max_size`` after being resized according to ``size``, then
@@ -572,7 +572,7 @@ def resized_crop(
             :class:`torchvision.transforms.InterpolationMode`.
             Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``,
             ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
-            For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
+            For backward compatibility integer values (e.g. ``PIL.Image(.Resampling).NEAREST``) are still acceptable.
 
     Returns:
         PIL Image or Tensor: Cropped image.
@@ -652,7 +652,7 @@ def perspective(
         interpolation (InterpolationMode): Desired interpolation enum defined by
             :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
             If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
-            For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
+            For backward compatibility integer values (e.g. ``PIL.Image(.Resampling).NEAREST``) are still acceptable.
         fill (sequence or number, optional): Pixel fill value for the area outside the transformed
             image. If given a number, the value is used for all bands respectively.
 
@@ -1012,7 +1012,7 @@ def rotate(
         interpolation (InterpolationMode): Desired interpolation enum defined by
             :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
             If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
-            For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
+            For backward compatibility integer values (e.g. ``PIL.Image(.Resampling).NEAREST``) are still acceptable.
         expand (bool, optional): Optional expansion flag.
             If true, expands the output image to make it large enough to hold the entire rotated image.
             If false or omitted, make the output image the same size as the input image.
@@ -1105,7 +1105,7 @@ def affine(
         interpolation (InterpolationMode): Desired interpolation enum defined by
             :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
             If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
-            For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
+            For backward compatibility integer values (e.g. ``PIL.Image(.Resampling).NEAREST``) are still acceptable.
         fill (sequence or number, optional): Pixel fill value for the area outside the transformed
             image. If given a number, the value is used for all bands respectively.
 

torchvision/transforms/functional_pil.py

@@ -10,6 +10,7 @@
     import accimage
 except ImportError:
     accimage = None
+from . import _pil_constants
 
 
 @torch.jit.unused
@@ -54,15 +55,15 @@ def hflip(img: Image.Image) -> Image.Image:
     if not _is_pil_image(img):
         raise TypeError(f"img should be PIL Image. Got {type(img)}")
 
-    return img.transpose(Image.FLIP_LEFT_RIGHT)
+    return img.transpose(_pil_constants.FLIP_LEFT_RIGHT)
 
 
 @torch.jit.unused
 def vflip(img: Image.Image) -> Image.Image:
     if not _is_pil_image(img):
         raise TypeError(f"img should be PIL Image. Got {type(img)}")
 
-    return img.transpose(Image.FLIP_TOP_BOTTOM)
+    return img.transpose(_pil_constants.FLIP_TOP_BOTTOM)
 
 
 @torch.jit.unused
@@ -240,7 +241,7 @@ def crop(
 def resize(
     img: Image.Image,
     size: Union[Sequence[int], int],
-    interpolation: int = Image.BILINEAR,
+    interpolation: int = _pil_constants.BILINEAR,
     max_size: Optional[int] = None,
 ) -> Image.Image:
 
@@ -314,7 +315,7 @@ def _parse_fill(
 def affine(
     img: Image.Image,
     matrix: List[float],
-    interpolation: int = Image.NEAREST,
+    interpolation: int = _pil_constants.NEAREST,
     fill: Optional[Union[float, List[float], Tuple[float, ...]]] = 0,
 ) -> Image.Image:
 
@@ -323,14 +324,14 @@ def affine(
 
     output_size = img.size
     opts = _parse_fill(fill, img)
-    return img.transform(output_size, Image.AFFINE, matrix, interpolation, **opts)
+    return img.transform(output_size, _pil_constants.AFFINE, matrix, interpolation, **opts)
 
 
 @torch.jit.unused
 def rotate(
     img: Image.Image,
     angle: float,
-    interpolation: int = Image.NEAREST,
+    interpolation: int = _pil_constants.NEAREST,
     expand: bool = False,
     center: Optional[Tuple[int, int]] = None,
     fill: Optional[Union[float, List[float], Tuple[float, ...]]] = 0,
@@ -347,7 +348,7 @@ def rotate(
 def perspective(
     img: Image.Image,
     perspective_coeffs: float,
-    interpolation: int = Image.BICUBIC,
+    interpolation: int = _pil_constants.BICUBIC,
     fill: Optional[Union[float, List[float], Tuple[float, ...]]] = 0,
 ) -> Image.Image:
 
@@ -356,7 +357,7 @@ def perspective(
 
     opts = _parse_fill(fill, img)
 
-    return img.transform(img.size, Image.PERSPECTIVE, perspective_coeffs, interpolation, **opts)
+    return img.transform(img.size, _pil_constants.PERSPECTIVE, perspective_coeffs, interpolation, **opts)
 
 
 @torch.jit.unused

torchvision/transforms/transforms.py

@@ -17,7 +17,6 @@
 from . import functional as F
 from .functional import InterpolationMode, _interpolation_modes_from_int
 
-
 __all__ = [
     "Compose",
     "ToTensor",
@@ -298,7 +297,7 @@ class Resize(torch.nn.Module):
             :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
             If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` and
             ``InterpolationMode.BICUBIC`` are supported.
-            For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
+            For backward compatibility integer values (e.g. ``PIL.Image(.Resampling).NEAREST``) are still acceptable.
         max_size (int, optional): The maximum allowed for the longer edge of
             the resized image: if the longer edge of the image is greater
             than ``max_size`` after being resized according to ``size``, then
@@ -755,7 +754,7 @@ class RandomPerspective(torch.nn.Module):
         interpolation (InterpolationMode): Desired interpolation enum defined by
             :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
             If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
-            For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
+            For backward compatibility integer values (e.g. ``PIL.Image(.Resampling).NEAREST``) are still acceptable.
         fill (sequence or number): Pixel fill value for the area outside the transformed
             image. Default is ``0``. If given a number, the value is used for all bands respectively.
     """
@@ -869,7 +868,7 @@ class RandomResizedCrop(torch.nn.Module):
             :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
             If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` and
             ``InterpolationMode.BICUBIC`` are supported.
-            For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
+            For backward compatibility integer values (e.g. ``PIL.Image(.Resampling).NEAREST``) are still acceptable.
 
     """
 
@@ -1268,7 +1267,7 @@ class RandomRotation(torch.nn.Module):
         interpolation (InterpolationMode): Desired interpolation enum defined by
             :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
             If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
-            For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
+            For backward compatibility integer values (e.g. ``PIL.Image(.Resampling).NEAREST``) are still acceptable.
         expand (bool, optional): Optional expansion flag.
             If true, expands the output to make it large enough to hold the entire rotated image.
             If false or omitted, make the output image the same size as the input image.
@@ -1389,7 +1388,7 @@ class RandomAffine(torch.nn.Module):
         interpolation (InterpolationMode): Desired interpolation enum defined by
             :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
             If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
-            For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
+            For backward compatibility integer values (e.g. ``PIL.Image(.Resampling).NEAREST``) are still acceptable.
         fill (sequence or number): Pixel fill value for the area outside the transformed
             image. Default is ``0``. If given a number, the value is used for all bands respectively.
         fillcolor (sequence or number, optional):