Plural to singular name change. (#3055)

Author: datumbox

test/test_functional_tensor.py

@@ -9,12 +9,12 @@
 import torchvision.transforms.functional_tensor as F_t
 import torchvision.transforms.functional_pil as F_pil
 import torchvision.transforms.functional as F
-from torchvision.transforms import InterpolationModes
+from torchvision.transforms import InterpolationMode
 
 from common_utils import TransformsTester
 
 
-NEAREST, BILINEAR, BICUBIC = InterpolationModes.NEAREST, InterpolationModes.BILINEAR, InterpolationModes.BICUBIC
+NEAREST, BILINEAR, BICUBIC = InterpolationMode.NEAREST, InterpolationMode.BILINEAR, InterpolationMode.BICUBIC
 
 
 class Tester(TransformsTester):
@@ -419,7 +419,7 @@ def test_resize(self):
                     )
 
         # assert changed type warning
-        with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationModes"):
+        with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationMode"):
             res1 = F.resize(tensor, size=32, interpolation=2)
             res2 = F.resize(tensor, size=32, interpolation=BILINEAR)
             self.assertTrue(res1.equal(res2))
@@ -626,7 +626,7 @@ def test_affine(self):
             self.assertTrue(res1.equal(res2))
 
         # assert changed type warning
-        with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationModes"):
+        with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationMode"):
             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)
             self.assertTrue(res1.equal(res2))
@@ -714,7 +714,7 @@ def test_rotate(self):
             self.assertTrue(res1.equal(res2))
 
         # assert changed type warning
-        with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationModes"):
+        with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationMode"):
             res1 = F.rotate(tensor, 45, interpolation=2)
             res2 = F.rotate(tensor, 45, interpolation=BILINEAR)
             self.assertTrue(res1.equal(res2))
@@ -788,7 +788,7 @@ def test_perspective(self):
         # assert changed type warning
         spoints = [[0, 0], [33, 0], [33, 25], [0, 25]]
         epoints = [[3, 2], [32, 3], [30, 24], [2, 25]]
-        with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationModes"):
+        with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationMode"):
             res1 = F.perspective(tensor, startpoints=spoints, endpoints=epoints, interpolation=2)
             res2 = F.perspective(tensor, startpoints=spoints, endpoints=epoints, interpolation=BILINEAR)
             self.assertTrue(res1.equal(res2))

test/test_transforms.py

@@ -1500,12 +1500,12 @@ def test_random_rotation(self):
         # assert deprecation warning and non-BC
         with self.assertWarnsRegex(UserWarning, r"Argument resample is deprecated and will be removed"):
             t = transforms.RandomRotation((-10, 10), resample=2)
-            self.assertEqual(t.interpolation, transforms.InterpolationModes.BILINEAR)
+            self.assertEqual(t.interpolation, transforms.InterpolationMode.BILINEAR)
 
         # assert changed type warning
-        with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationModes"):
+        with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationMode"):
             t = transforms.RandomRotation((-10, 10), interpolation=2)
-            self.assertEqual(t.interpolation, transforms.InterpolationModes.BILINEAR)
+            self.assertEqual(t.interpolation, transforms.InterpolationMode.BILINEAR)
 
     def test_random_affine(self):
 
@@ -1547,22 +1547,22 @@ def test_random_affine(self):
         # Checking if RandomAffine can be printed as string
         t.__repr__()
 
-        t = transforms.RandomAffine(10, interpolation=transforms.InterpolationModes.BILINEAR)
+        t = transforms.RandomAffine(10, interpolation=transforms.InterpolationMode.BILINEAR)
         self.assertIn("bilinear", t.__repr__())
 
         # assert deprecation warning and non-BC
         with self.assertWarnsRegex(UserWarning, r"Argument resample is deprecated and will be removed"):
             t = transforms.RandomAffine(10, resample=2)
-            self.assertEqual(t.interpolation, transforms.InterpolationModes.BILINEAR)
+            self.assertEqual(t.interpolation, transforms.InterpolationMode.BILINEAR)
 
         with self.assertWarnsRegex(UserWarning, r"Argument fillcolor is deprecated and will be removed"):
             t = transforms.RandomAffine(10, fillcolor=10)
             self.assertEqual(t.fill, 10)
 
         # assert changed type warning
-        with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationModes"):
+        with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationMode"):
             t = transforms.RandomAffine(10, interpolation=2)
-            self.assertEqual(t.interpolation, transforms.InterpolationModes.BILINEAR)
+            self.assertEqual(t.interpolation, transforms.InterpolationMode.BILINEAR)
 
     def test_to_grayscale(self):
         """Unit tests for grayscale transform"""

test/test_transforms_tensor.py

@@ -2,7 +2,7 @@
 import torch
 from torchvision import transforms as T
 from torchvision.transforms import functional as F
-from torchvision.transforms import InterpolationModes
+from torchvision.transforms import InterpolationMode
 
 import numpy as np
 
@@ -11,7 +11,7 @@
 from common_utils import TransformsTester, get_tmp_dir, int_dtypes, float_dtypes
 
 
-NEAREST, BILINEAR, BICUBIC = InterpolationModes.NEAREST, InterpolationModes.BILINEAR, InterpolationModes.BICUBIC
+NEAREST, BILINEAR, BICUBIC = InterpolationMode.NEAREST, InterpolationMode.BILINEAR, InterpolationMode.BICUBIC
 
 
 class Tester(TransformsTester):

torchvision/transforms/functional.py

@@ -20,7 +20,7 @@
 from . import functional_tensor as F_t
 
 
-class InterpolationModes(Enum):
+class InterpolationMode(Enum):
     """Interpolation modes
     """
     NEAREST = "nearest"
@@ -33,26 +33,26 @@ class InterpolationModes(Enum):
 
 
 # TODO: Once torchscript supports Enums with staticmethod
-# this can be put into InterpolationModes as staticmethod
-def _interpolation_modes_from_int(i: int) -> InterpolationModes:
+# this can be put into InterpolationMode as staticmethod
+def _interpolation_modes_from_int(i: int) -> InterpolationMode:
     inverse_modes_mapping = {
-        0: InterpolationModes.NEAREST,
-        2: InterpolationModes.BILINEAR,
-        3: InterpolationModes.BICUBIC,
-        4: InterpolationModes.BOX,
-        5: InterpolationModes.HAMMING,
-        1: InterpolationModes.LANCZOS,
+        0: InterpolationMode.NEAREST,
+        2: InterpolationMode.BILINEAR,
+        3: InterpolationMode.BICUBIC,
+        4: InterpolationMode.BOX,
+        5: InterpolationMode.HAMMING,
+        1: InterpolationMode.LANCZOS,
     }
     return inverse_modes_mapping[i]
 
 
 pil_modes_mapping = {
-    InterpolationModes.NEAREST: 0,
-    InterpolationModes.BILINEAR: 2,
-    InterpolationModes.BICUBIC: 3,
-    InterpolationModes.BOX: 4,
-    InterpolationModes.HAMMING: 5,
-    InterpolationModes.LANCZOS: 1,
+    InterpolationMode.NEAREST: 0,
+    InterpolationMode.BILINEAR: 2,
+    InterpolationMode.BICUBIC: 3,
+    InterpolationMode.BOX: 4,
+    InterpolationMode.HAMMING: 5,
+    InterpolationMode.LANCZOS: 1,
 }
 
 _is_pil_image = F_pil._is_pil_image
@@ -329,7 +329,7 @@ def normalize(tensor: Tensor, mean: List[float], std: List[float], inplace: bool
     return tensor
 
 
-def resize(img: Tensor, size: List[int], interpolation: InterpolationModes = InterpolationModes.BILINEAR) -> Tensor:
+def resize(img: Tensor, size: List[int], interpolation: InterpolationMode = InterpolationMode.BILINEAR) -> Tensor:
     r"""Resize the input image to the given size.
     The image can be a PIL Image or a torch Tensor, in which case it is expected
     to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
@@ -343,10 +343,10 @@ def resize(img: Tensor, size: List[int], interpolation: InterpolationModes = Int
             :math:`\left(\text{size} \times \frac{\text{height}}{\text{width}}, \text{size}\right)`.
             In torchscript mode size as single int is not supported, use a tuple or
             list of length 1: ``[size, ]``.
-        interpolation (InterpolationModes): Desired interpolation enum defined by
-            :class:`torchvision.transforms.InterpolationModes`.
-            Default is ``InterpolationModes.BILINEAR``. If input is Tensor, only ``InterpolationModes.NEAREST``,
-            ``InterpolationModes.BILINEAR`` and ``InterpolationModes.BICUBIC`` are supported.
+        interpolation (InterpolationMode): Desired interpolation enum defined by
+            :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.
 
     Returns:
@@ -355,13 +355,13 @@ def resize(img: Tensor, size: List[int], interpolation: InterpolationModes = Int
     # Backward compatibility with integer value
     if isinstance(interpolation, int):
         warnings.warn(
-            "Argument interpolation should be of type InterpolationModes instead of int. "
-            "Please, use InterpolationModes enum."
+            "Argument interpolation should be of type InterpolationMode instead of int. "
+            "Please, use InterpolationMode enum."
         )
         interpolation = _interpolation_modes_from_int(interpolation)
 
-    if not isinstance(interpolation, InterpolationModes):
-        raise TypeError("Argument interpolation should be a InterpolationModes")
+    if not isinstance(interpolation, InterpolationMode):
+        raise TypeError("Argument interpolation should be a InterpolationMode")
 
     if not isinstance(img, torch.Tensor):
         pil_interpolation = pil_modes_mapping[interpolation]
@@ -475,7 +475,7 @@ def center_crop(img: Tensor, output_size: List[int]) -> Tensor:
 
 def resized_crop(
         img: Tensor, top: int, left: int, height: int, width: int, size: List[int],
-        interpolation: InterpolationModes = InterpolationModes.BILINEAR
+        interpolation: InterpolationMode = InterpolationMode.BILINEAR
 ) -> Tensor:
     """Crop the given image and resize it to desired size.
     The image can be a PIL Image or a Tensor, in which case it is expected
@@ -490,10 +490,10 @@ def resized_crop(
         height (int): Height of the crop box.
         width (int): Width of the crop box.
         size (sequence or int): Desired output size. Same semantics as ``resize``.
-        interpolation (InterpolationModes): Desired interpolation enum defined by
-            :class:`torchvision.transforms.InterpolationModes`.
-            Default is ``InterpolationModes.BILINEAR``. If input is Tensor, only ``InterpolationModes.NEAREST``,
-            ``InterpolationModes.BILINEAR`` and ``InterpolationModes.BICUBIC`` are supported.
+        interpolation (InterpolationMode): Desired interpolation enum defined by
+            :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.
 
     Returns:
@@ -556,7 +556,7 @@ def perspective(
         img: Tensor,
         startpoints: List[List[int]],
         endpoints: List[List[int]],
-        interpolation: InterpolationModes = InterpolationModes.BILINEAR,
+        interpolation: InterpolationMode = InterpolationMode.BILINEAR,
         fill: Optional[int] = None
 ) -> Tensor:
     """Perform perspective transform of the given image.
@@ -569,9 +569,9 @@ def perspective(
             ``[top-left, top-right, bottom-right, bottom-left]`` of the original image.
         endpoints (list of list of ints): List containing four lists of two integers corresponding to four corners
             ``[top-left, top-right, bottom-right, bottom-left]`` of the transformed image.
-        interpolation (InterpolationModes): Desired interpolation enum defined by
-            :class:`torchvision.transforms.InterpolationModes`. Default is ``InterpolationModes.BILINEAR``.
-            If input is Tensor, only ``InterpolationModes.NEAREST``, ``InterpolationModes.BILINEAR`` are supported.
+        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.
         fill (n-tuple or int or float): Pixel fill value for area outside the rotated
             image. If int or float, the value is used for all bands respectively.
@@ -587,13 +587,13 @@ def perspective(
     # Backward compatibility with integer value
     if isinstance(interpolation, int):
         warnings.warn(
-            "Argument interpolation should be of type InterpolationModes instead of int. "
-            "Please, use InterpolationModes enum."
+            "Argument interpolation should be of type InterpolationMode instead of int. "
+            "Please, use InterpolationMode enum."
         )
         interpolation = _interpolation_modes_from_int(interpolation)
 
-    if not isinstance(interpolation, InterpolationModes):
-        raise TypeError("Argument interpolation should be a InterpolationModes")
+    if not isinstance(interpolation, InterpolationMode):
+        raise TypeError("Argument interpolation should be a InterpolationMode")
 
     if not isinstance(img, torch.Tensor):
         pil_interpolation = pil_modes_mapping[interpolation]
@@ -869,7 +869,7 @@ def _get_inverse_affine_matrix(
 
 
 def rotate(
-        img: Tensor, angle: float, interpolation: InterpolationModes = InterpolationModes.NEAREST,
+        img: Tensor, angle: float, interpolation: InterpolationMode = InterpolationMode.NEAREST,
         expand: bool = False, center: Optional[List[int]] = None,
         fill: Optional[int] = None, resample: Optional[int] = None
 ) -> Tensor:
@@ -880,9 +880,9 @@ def rotate(
     Args:
         img (PIL Image or Tensor): image to be rotated.
         angle (float or int): rotation angle value in degrees, counter-clockwise.
-        interpolation (InterpolationModes): Desired interpolation enum defined by
-            :class:`torchvision.transforms.InterpolationModes`. Default is ``InterpolationModes.NEAREST``.
-            If input is Tensor, only ``InterpolationModes.NEAREST``, ``InterpolationModes.BILINEAR`` are supported.
+        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.
         expand (bool, optional): Optional expansion flag.
             If true, expands the output image to make it large enough to hold the entire rotated image.
@@ -913,8 +913,8 @@ def rotate(
     # Backward compatibility with integer value
     if isinstance(interpolation, int):
         warnings.warn(
-            "Argument interpolation should be of type InterpolationModes instead of int. "
-            "Please, use InterpolationModes enum."
+            "Argument interpolation should be of type InterpolationMode instead of int. "
+            "Please, use InterpolationMode enum."
         )
         interpolation = _interpolation_modes_from_int(interpolation)
 
@@ -924,8 +924,8 @@ def rotate(
     if center is not None and not isinstance(center, (list, tuple)):
         raise TypeError("Argument center should be a sequence")
 
-    if not isinstance(interpolation, InterpolationModes):
-        raise TypeError("Argument interpolation should be a InterpolationModes")
+    if not isinstance(interpolation, InterpolationMode):
+        raise TypeError("Argument interpolation should be a InterpolationMode")
 
     if not isinstance(img, torch.Tensor):
         pil_interpolation = pil_modes_mapping[interpolation]
@@ -945,7 +945,7 @@ def rotate(
 
 def affine(
         img: Tensor, angle: float, translate: List[int], scale: float, shear: List[float],
-        interpolation: InterpolationModes = InterpolationModes.NEAREST, fill: Optional[int] = None,
+        interpolation: InterpolationMode = InterpolationMode.NEAREST, fill: Optional[int] = None,
         resample: Optional[int] = None, fillcolor: Optional[int] = None
 ) -> Tensor:
     """Apply affine transformation on the image keeping image center invariant.
@@ -960,9 +960,9 @@ def affine(
         shear (float or tuple or list): shear angle value in degrees between -180 to 180, clockwise direction.
             If a tuple of list is specified, the first value corresponds to a shear parallel to the x axis, while
             the second value corresponds to a shear parallel to the y axis.
-        interpolation (InterpolationModes): Desired interpolation enum defined by
-            :class:`torchvision.transforms.InterpolationModes`. Default is ``InterpolationModes.NEAREST``.
-            If input is Tensor, only ``InterpolationModes.NEAREST``, ``InterpolationModes.BILINEAR`` are supported.
+        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.
         fill (int): Optional fill color for the area outside the transform in the output image (Pillow>=5.0.0).
             This option is not supported for Tensor input. Fill value for the area outside the transform in the output
@@ -984,8 +984,8 @@ def affine(
     # Backward compatibility with integer value
     if isinstance(interpolation, int):
         warnings.warn(
-            "Argument interpolation should be of type InterpolationModes instead of int. "
-            "Please, use InterpolationModes enum."
+            "Argument interpolation should be of type InterpolationMode instead of int. "
+            "Please, use InterpolationMode enum."
         )
         interpolation = _interpolation_modes_from_int(interpolation)
 
@@ -1010,8 +1010,8 @@ def affine(
     if not isinstance(shear, (numbers.Number, (list, tuple))):
         raise TypeError("Shear should be either a single value or a sequence of two values")
 
-    if not isinstance(interpolation, InterpolationModes):
-        raise TypeError("Argument interpolation should be a InterpolationModes")
+    if not isinstance(interpolation, InterpolationMode):
+        raise TypeError("Argument interpolation should be a InterpolationMode")
 
     if isinstance(angle, int):
         angle = float(angle)