Deprecate int as interpolation argument type (#5974)

* Requested here https://github.com/pytorch/vision/pull/5898#discussion_r864765799

.

* Fix tests

* ufmt, not black
Co-authored-by: Philip Meier <github.pmeier@posteo.de>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

test/test_functional_tensor.py

@@ -156,7 +156,13 @@ class TestRotate:
     def test_rotate_interpolation_type(self):
         tensor, _ = _create_data(26, 26)
         # assert changed type warning
-        with pytest.warns(UserWarning, match=r"Argument interpolation should be of type InterpolationMode"):
+        with pytest.warns(
+            UserWarning,
+            match=re.escape(
+                "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
+                "Please use InterpolationMode enum."
+            ),
+        ):
             res1 = F.rotate(tensor, 45, interpolation=2)
             res2 = F.rotate(tensor, 45, interpolation=BILINEAR)
             assert_equal(res1, res2)
@@ -384,7 +390,13 @@ class TestAffine:
             assert_equal(res1, res2)
 
         # assert changed type warning
-        with pytest.warns(UserWarning, match=r"Argument interpolation should be of type InterpolationMode"):
+        with pytest.warns(
+            UserWarning,
+            match=re.escape(
+                "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
+                "Please use InterpolationMode enum."
+            ),
+        ):
             res1 = F.affine(tensor, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=2)
             res2 = F.affine(tensor, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=BILINEAR)
             assert_equal(res1, res2)
@@ -504,7 +516,13 @@ def test_perspective_interpolation_warning():
     spoints = [[0, 0], [33, 0], [33, 25], [0, 25]]
     epoints = [[3, 2], [32, 3], [30, 24], [2, 25]]
     tensor = torch.randint(0, 256, (3, 26, 26))
-    with pytest.warns(UserWarning, match="Argument interpolation should be of type InterpolationMode"):
+    with pytest.warns(
+        UserWarning,
+        match=re.escape(
+            "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
+            "Please use InterpolationMode enum."
+        ),
+    ):
         res1 = F.perspective(tensor, startpoints=spoints, endpoints=epoints, interpolation=2)
         res2 = F.perspective(tensor, startpoints=spoints, endpoints=epoints, interpolation=BILINEAR)
         assert_equal(res1, res2)
@@ -584,7 +602,13 @@ def test_resize_asserts(device):
     tensor, pil_img = _create_data(26, 36, device=device)
 
     # assert changed type warning
-    with pytest.warns(UserWarning, match=r"Argument interpolation should be of type InterpolationMode"):
+    with pytest.warns(
+        UserWarning,
+        match=re.escape(
+            "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
+            "Please use InterpolationMode enum."
+        ),
+    ):
         res1 = F.resize(tensor, size=32, interpolation=2)
 
     res2 = F.resize(tensor, size=32, interpolation=BILINEAR)

test/test_transforms.py

@@ -1878,7 +1878,13 @@ def test_random_rotation():
         assert t.interpolation == transforms.InterpolationMode.BILINEAR
 
     # assert changed type warning
-    with pytest.warns(UserWarning, match=r"Argument interpolation should be of type InterpolationMode"):
+    with pytest.warns(
+        UserWarning,
+        match=re.escape(
+            "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
+            "Please use InterpolationMode enum."
+        ),
+    ):
         t = transforms.RandomRotation((-10, 10), interpolation=2)
         assert t.interpolation == transforms.InterpolationMode.BILINEAR
 
@@ -2233,7 +2239,13 @@ def test_random_affine():
         assert t.fill == 10
 
     # assert changed type warning
-    with pytest.warns(UserWarning, match=r"Argument interpolation should be of type InterpolationMode"):
+    with pytest.warns(
+        UserWarning,
+        match=re.escape(
+            "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
+            "Please use InterpolationMode enum."
+        ),
+    ):
         t = transforms.RandomAffine(10, interpolation=2)
         assert t.interpolation == transforms.InterpolationMode.BILINEAR
 

torchvision/transforms/functional.py

@@ -392,7 +392,8 @@ 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[.Resampling].NEAREST``) are still acceptable.
+            For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
+            but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
         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
@@ -414,8 +415,8 @@ def resize(
     # Backward compatibility with integer value
     if isinstance(interpolation, int):
         warnings.warn(
-            "Argument interpolation should be of type InterpolationMode instead of int. "
-            "Please, use InterpolationMode enum."
+            "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
+            "Please use InterpolationMode enum."
         )
         interpolation = _interpolation_modes_from_int(interpolation)
 
@@ -572,8 +573,8 @@ 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[.Resampling].NEAREST``) are still acceptable.
-
+            For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
+            but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
     Returns:
         PIL Image or Tensor: Cropped image.
     """
@@ -652,7 +653,8 @@ 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[.Resampling].NEAREST``) are still acceptable.
+            For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
+            but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
         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.
 
@@ -671,8 +673,8 @@ def perspective(
     # Backward compatibility with integer value
     if isinstance(interpolation, int):
         warnings.warn(
-            "Argument interpolation should be of type InterpolationMode instead of int. "
-            "Please, use InterpolationMode enum."
+            "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
+            "Please use InterpolationMode enum."
         )
         interpolation = _interpolation_modes_from_int(interpolation)
 
@@ -1012,7 +1014,8 @@ 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[.Resampling].NEAREST``) are still acceptable.
+            For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
+            but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
         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.
@@ -1048,8 +1051,8 @@ def rotate(
     # Backward compatibility with integer value
     if isinstance(interpolation, int):
         warnings.warn(
-            "Argument interpolation should be of type InterpolationMode instead of int. "
-            "Please, use InterpolationMode enum."
+            "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
+            "Please use InterpolationMode enum."
         )
         interpolation = _interpolation_modes_from_int(interpolation)
 
@@ -1105,7 +1108,8 @@ 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[.Resampling].NEAREST``) are still acceptable.
+            For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
+            but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
         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.
 
@@ -1137,8 +1141,8 @@ def affine(
     # Backward compatibility with integer value
     if isinstance(interpolation, int):
         warnings.warn(
-            "Argument interpolation should be of type InterpolationMode instead of int. "
-            "Please, use InterpolationMode enum."
+            "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
+            "Please use InterpolationMode enum."
         )
         interpolation = _interpolation_modes_from_int(interpolation)
 

torchvision/transforms/transforms.py

@@ -297,7 +297,8 @@ 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[.Resampling].NEAREST``) are still acceptable.
+            For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
+            but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
         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
@@ -329,8 +330,8 @@ class Resize(torch.nn.Module):
         # Backward compatibility with integer value
         if isinstance(interpolation, int):
             warnings.warn(
-                "Argument interpolation should be of type InterpolationMode instead of int. "
-                "Please, use InterpolationMode enum."
+                "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
+                "Please use InterpolationMode enum."
             )
             interpolation = _interpolation_modes_from_int(interpolation)
 
@@ -754,7 +755,8 @@ 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[.Resampling].NEAREST``) are still acceptable.
+            For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
+            but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
         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.
     """
@@ -767,8 +769,8 @@ class RandomPerspective(torch.nn.Module):
         # Backward compatibility with integer value
         if isinstance(interpolation, int):
             warnings.warn(
-                "Argument interpolation should be of type InterpolationMode instead of int. "
-                "Please, use InterpolationMode enum."
+                "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
+                "Please use InterpolationMode enum."
             )
             interpolation = _interpolation_modes_from_int(interpolation)
 
@@ -868,8 +870,8 @@ 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[.Resampling].NEAREST``) are still acceptable.
-
+            For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
+            but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
     """
 
     def __init__(self, size, scale=(0.08, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0), interpolation=InterpolationMode.BILINEAR):
@@ -887,8 +889,8 @@ class RandomResizedCrop(torch.nn.Module):
         # Backward compatibility with integer value
         if isinstance(interpolation, int):
             warnings.warn(
-                "Argument interpolation should be of type InterpolationMode instead of int. "
-                "Please, use InterpolationMode enum."
+                "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
+                "Please use InterpolationMode enum."
             )
             interpolation = _interpolation_modes_from_int(interpolation)
 
@@ -1267,7 +1269,8 @@ 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[.Resampling].NEAREST``) are still acceptable.
+            For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
+            but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
         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.
@@ -1300,8 +1303,8 @@ class RandomRotation(torch.nn.Module):
         # Backward compatibility with integer value
         if isinstance(interpolation, int):
             warnings.warn(
-                "Argument interpolation should be of type InterpolationMode instead of int. "
-                "Please, use InterpolationMode enum."
+                "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
+                "Please use InterpolationMode enum."
             )
             interpolation = _interpolation_modes_from_int(interpolation)
 
@@ -1388,7 +1391,8 @@ 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[.Resampling].NEAREST``) are still acceptable.
+            For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
+            but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
         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):
@@ -1429,8 +1433,8 @@ class RandomAffine(torch.nn.Module):
         # Backward compatibility with integer value
         if isinstance(interpolation, int):
             warnings.warn(
-                "Argument interpolation should be of type InterpolationMode instead of int. "
-                "Please, use InterpolationMode enum."
+                "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
+                "Please use InterpolationMode enum."
             )
             interpolation = _interpolation_modes_from_int(interpolation)
 
@@ -1727,9 +1731,7 @@ class RandomErasing(torch.nn.Module):
 
             # cast self.value to script acceptable type
             if isinstance(self.value, (int, float)):
-                value = [
-                    self.value,
-                ]
+                value = [self.value]
             elif isinstance(self.value, str):
                 value = None
             elif isinstance(self.value, tuple):