Change antialias default from None to True (#7949)

test/smoke_test.py

@@ -59,7 +59,7 @@ def smoke_test_torchvision_resnet50_classify(device: str = "cpu") -> None:
     model.eval()
 
     # Step 2: Initialize the inference transforms
-    preprocess = weights.transforms()
+    preprocess = weights.transforms(antialias=(device != "mps"))  # antialias not supported on MPS
 
     # Step 3: Apply inference preprocessing transforms
     batch = preprocess(img).unsqueeze(0)

test/test_functional_tensor.py

@@ -2,7 +2,6 @@ import colorsys
 import itertools
 import math
 import os
-import warnings
 from functools import partial
 from typing import Sequence
 
@@ -569,23 +568,6 @@ def test_resize_antialias(device, dt, size, interpolation):
     assert_equal(resized_tensor, resize_result)
 
 
-def test_resize_antialias_default_warning():
-
-    img = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8)
-
-    match = "The default value of the antialias"
-    with pytest.warns(UserWarning, match=match):
-        F.resize(img, size=(20, 20))
-    with pytest.warns(UserWarning, match=match):
-        F.resized_crop(img, 0, 0, 10, 10, size=(20, 20))
-
-    # For modes that aren't bicubic or bilinear, don't throw a warning
-    with warnings.catch_warnings():
-        warnings.simplefilter("error")
-        F.resize(img, size=(20, 20), interpolation=NEAREST)
-        F.resized_crop(img, 0, 0, 10, 10, size=(20, 20), interpolation=NEAREST)
-
-
 def check_functional_vs_PIL_vs_scripted(
     fn, fn_pil, fn_t, config, device, dtype, channels=3, tol=2.0 + 1e-10, agg_method="max"
 ):

test/test_models.py

@@ -1057,25 +1057,5 @@ def test_raft(model_fn, scripted):
     _assert_expected(flow_pred.cpu(), name=model_fn.__name__, atol=1e-2, rtol=1)
 
 
-def test_presets_antialias():
-
-    img = torch.randint(0, 256, size=(1, 3, 224, 224), dtype=torch.uint8)
-
-    match = "The default value of the antialias parameter"
-    with pytest.warns(UserWarning, match=match):
-        models.ResNet18_Weights.DEFAULT.transforms()(img)
-    with pytest.warns(UserWarning, match=match):
-        models.segmentation.DeepLabV3_ResNet50_Weights.DEFAULT.transforms()(img)
-
-    with warnings.catch_warnings():
-        warnings.simplefilter("error")
-        models.ResNet18_Weights.DEFAULT.transforms(antialias=True)(img)
-        models.segmentation.DeepLabV3_ResNet50_Weights.DEFAULT.transforms(antialias=True)(img)
-
-        models.detection.FasterRCNN_ResNet50_FPN_Weights.DEFAULT.transforms()(img)
-        models.video.R3D_18_Weights.DEFAULT.transforms()(img)
-        models.optical_flow.Raft_Small_Weights.DEFAULT.transforms()(img, img)
-
-
 if __name__ == "__main__":
     pytest.main([__file__])

test/test_transforms.py

@@ -3,7 +3,6 @@ import os
 import random
 import re
 import textwrap
-import warnings
 from functools import partial
 
 import numpy as np
@@ -440,16 +439,6 @@ def test_resize_antialias_error():
         t(img)
 
 
-def test_resize_antialias_default_warning():
-
-    img = Image.new("RGB", size=(10, 10), color=127)
-    # We make sure we don't warn for PIL images since the default behaviour doesn't change
-    with warnings.catch_warnings():
-        warnings.simplefilter("error")
-        transforms.Resize((20, 20))(img)
-        transforms.RandomResizedCrop((20, 20))(img)
-
-
 @pytest.mark.parametrize("height, width", ((32, 64), (64, 32)))
 def test_resize_size_equals_small_edge_size(height, width):
     # Non-regression test for https://github.com/pytorch/vision/issues/5405

test/test_transforms_tensor.py

 import os
 import sys
-import warnings
 
 import numpy as np
 import PIL.Image
@@ -428,22 +427,6 @@ class TestResize:
         fn = T.RandomResizedCrop(size=[32], antialias=True)
         _test_fn_save_load(fn, tmpdir)
 
-    def test_antialias_default_warning(self):
-
-        img = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8)
-
-        match = "The default value of the antialias"
-        with pytest.warns(UserWarning, match=match):
-            T.Resize((20, 20))(img)
-        with pytest.warns(UserWarning, match=match):
-            T.RandomResizedCrop((20, 20))(img)
-
-        # For modes that aren't bicubic or bilinear, don't throw a warning
-        with warnings.catch_warnings():
-            warnings.simplefilter("error")
-            T.Resize((20, 20), interpolation=NEAREST)(img)
-            T.RandomResizedCrop((20, 20), interpolation=NEAREST)(img)
-
 
 def _test_random_affine_helper(device, **kwargs):
     tensor = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8, device=device)

test/test_transforms_v2.py

@@ -2,7 +2,6 @@ import itertools
 import pathlib
 import pickle
 import random
-import warnings
 
 import numpy as np
 
@@ -726,46 +725,6 @@ class TestUniformTemporalSubsample:
         assert output.dtype == inpt.dtype
 
 
-# TODO: remove this test in 0.17 when the default of antialias changes to True
-def test_antialias_warning():
-    pil_img = PIL.Image.new("RGB", size=(10, 10), color=127)
-    tensor_img = torch.randint(0, 256, size=(3, 10, 10), dtype=torch.uint8)
-    tensor_video = torch.randint(0, 256, size=(2, 3, 10, 10), dtype=torch.uint8)
-
-    match = "The default value of the antialias parameter"
-    with pytest.warns(UserWarning, match=match):
-        transforms.RandomResizedCrop((20, 20))(tensor_img)
-    with pytest.warns(UserWarning, match=match):
-        transforms.ScaleJitter((20, 20))(tensor_img)
-    with pytest.warns(UserWarning, match=match):
-        transforms.RandomShortestSize((20, 20))(tensor_img)
-    with pytest.warns(UserWarning, match=match):
-        transforms.RandomResize(10, 20)(tensor_img)
-
-    with pytest.warns(UserWarning, match=match):
-        F.resized_crop(tv_tensors.Image(tensor_img), 0, 0, 10, 10, (20, 20))
-
-    with pytest.warns(UserWarning, match=match):
-        F.resize(tv_tensors.Video(tensor_video), (20, 20))
-    with pytest.warns(UserWarning, match=match):
-        F.resized_crop(tv_tensors.Video(tensor_video), 0, 0, 10, 10, (20, 20))
-
-    with warnings.catch_warnings():
-        warnings.simplefilter("error")
-        transforms.RandomResizedCrop((20, 20))(pil_img)
-        transforms.ScaleJitter((20, 20))(pil_img)
-        transforms.RandomShortestSize((20, 20))(pil_img)
-        transforms.RandomResize(10, 20)(pil_img)
-
-        transforms.RandomResizedCrop((20, 20), antialias=True)(tensor_img)
-        transforms.ScaleJitter((20, 20), antialias=True)(tensor_img)
-        transforms.RandomShortestSize((20, 20), antialias=True)(tensor_img)
-        transforms.RandomResize(10, 20, antialias=True)(tensor_img)
-
-        F.resized_crop(tv_tensors.Image(tensor_img), 0, 0, 10, 10, (20, 20), antialias=True)
-        F.resized_crop(tv_tensors.Video(tensor_video), 0, 0, 10, 10, (20, 20), antialias=True)
-
-
 @pytest.mark.parametrize("image_type", (PIL.Image, torch.Tensor, tv_tensors.Image))
 @pytest.mark.parametrize("label_type", (torch.Tensor, int))
 @pytest.mark.parametrize("dataset_return_type", (dict, tuple))

test/test_transforms_v2_refactored.py

@@ -19,7 +19,6 @@ import torchvision.ops
 import torchvision.transforms.v2 as transforms
 from common_utils import (
     assert_equal,
-    assert_no_warnings,
     cache,
     cpu_and_cuda,
     freeze_rng_state,
@@ -350,12 +349,6 @@ INTERPOLATION_MODES = [
 ]
 
 
-@contextlib.contextmanager
-def assert_warns_antialias_default_value():
-    with pytest.warns(UserWarning, match="The default value of the antialias parameter of all the resizing transforms"):
-        yield
-
-
 def reference_affine_bounding_boxes_helper(bounding_boxes, *, affine_matrix, new_canvas_size=None, clamp=True):
     format = bounding_boxes.format
     canvas_size = new_canvas_size or bounding_boxes.canvas_size
@@ -684,23 +677,6 @@ class TestResize:
         with pytest.raises(ValueError, match=match):
             F.resize(make_input(self.INPUT_SIZE), size=size, max_size=max_size, antialias=True)
 
-    @pytest.mark.parametrize("interpolation", INTERPOLATION_MODES)
-    @pytest.mark.parametrize(
-        "make_input",
-        [make_image_tensor, make_image, make_video],
-    )
-    def test_antialias_warning(self, interpolation, make_input):
-        with (
-            assert_warns_antialias_default_value()
-            if interpolation in {transforms.InterpolationMode.BILINEAR, transforms.InterpolationMode.BICUBIC}
-            else assert_no_warnings()
-        ):
-            F.resize(
-                make_input(self.INPUT_SIZE),
-                size=self.OUTPUT_SIZES[0],
-                interpolation=interpolation,
-            )
-
     @pytest.mark.parametrize("interpolation", INTERPOLATION_MODES)
     @pytest.mark.parametrize(
         "make_input",

torchvision/transforms/_functional_tensor.py

@@ -440,9 +440,7 @@ def resize(
     img: Tensor,
     size: List[int],
     interpolation: str = "bilinear",
-    # TODO: in v0.17, change the default to True. This will a private function
-    # by then, so we don't care about warning here.
-    antialias: Optional[bool] = None,
+    antialias: Optional[bool] = True,
 ) -> Tensor:
     _assert_image_tensor(img)
 

torchvision/transforms/_presets.py

@@ -2,7 +2,7 @@
 This file is part of the private API. Please do not use directly these classes as they will be modified on
 future versions without warning. The classes should be accessed only via the transforms argument of Weights.
 """
-from typing import Optional, Tuple, Union
+from typing import Optional, Tuple
 
 import torch
 from torch import nn, Tensor
@@ -44,7 +44,7 @@ class ImageClassification(nn.Module):
         mean: Tuple[float, ...] = (0.485, 0.456, 0.406),
         std: Tuple[float, ...] = (0.229, 0.224, 0.225),
         interpolation: InterpolationMode = InterpolationMode.BILINEAR,
-        antialias: Optional[Union[str, bool]] = "warn",
+        antialias: Optional[bool] = True,
     ) -> None:
         super().__init__()
         self.crop_size = [crop_size]
@@ -151,7 +151,7 @@ class SemanticSegmentation(nn.Module):
         mean: Tuple[float, ...] = (0.485, 0.456, 0.406),
         std: Tuple[float, ...] = (0.229, 0.224, 0.225),
         interpolation: InterpolationMode = InterpolationMode.BILINEAR,
-        antialias: Optional[Union[str, bool]] = "warn",
+        antialias: Optional[bool] = True,
     ) -> None:
         super().__init__()
         self.resize_size = [resize_size] if resize_size is not None else None

torchvision/transforms/functional.py

@@ -393,19 +393,12 @@ def resize(
     size: List[int],
     interpolation: InterpolationMode = InterpolationMode.BILINEAR,
     max_size: Optional[int] = None,
-    antialias: Optional[Union[str, bool]] = "warn",
+    antialias: Optional[bool] = True,
 ) -> Tensor:
     r"""Resize the input image to the given size.
     If the image is torch Tensor, it is expected
     to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
 
-    .. warning::
-        The output image might be different depending on its type: when downsampling, the interpolation of PIL images
-        and tensors is slightly different, because PIL applies antialiasing. This may lead to significant differences
-        in the performance of a network. Therefore, it is preferable to train and serve a model with the same input
-        types. See also below the ``antialias`` parameter, which can help making the output of PIL images and tensors
-        closer.
-
     Args:
         img (PIL Image or Tensor): Image to be resized.
         size (sequence or int): Desired output size. If size is a sequence like
@@ -437,7 +430,7 @@ def resize(
             tensors), antialiasing makes no sense and this parameter is ignored.
             Possible values are:
 
-            - ``True``: will apply antialiasing for bilinear or bicubic modes.
+            - ``True`` (default): will apply antialiasing for bilinear or bicubic modes.
               Other mode aren't affected. This is probably what you want to use.
             - ``False``: will not apply antialiasing for tensors on any mode. PIL
               images are still antialiased on bilinear or bicubic modes, because
@@ -446,8 +439,8 @@ def resize(
               PIL images. This value exists for legacy reasons and you probably
               don't want to use it unless you really know what you are doing.
 
-            The current default is ``None`` **but will change to** ``True`` **in
-            v0.17** for the PIL and Tensor backends to be consistent.
+            The default value changed from ``None`` to ``True`` in
+            v0.17, for the PIL and Tensor backends to be consistent.
 
     Returns:
         PIL Image or Tensor: Resized image.
@@ -481,8 +474,6 @@ def resize(
     if [image_height, image_width] == output_size:
         return img
 
-    antialias = _check_antialias(img, antialias, interpolation)
-
     if not isinstance(img, torch.Tensor):
         if antialias is False:
             warnings.warn("Anti-alias option is always applied for PIL Image input. Argument antialias is ignored.")
@@ -615,7 +606,7 @@ def resized_crop(
     width: int,
     size: List[int],
     interpolation: InterpolationMode = InterpolationMode.BILINEAR,
-    antialias: Optional[Union[str, bool]] = "warn",
+    antialias: Optional[bool] = True,
 ) -> Tensor:
     """Crop the given image and resize it to desired size.
     If the image is torch Tensor, it is expected
@@ -643,7 +634,7 @@ def resized_crop(
             tensors), antialiasing makes no sense and this parameter is ignored.
             Possible values are:
 
-            - ``True``: will apply antialiasing for bilinear or bicubic modes.
+            - ``True`` (default): will apply antialiasing for bilinear or bicubic modes.
               Other mode aren't affected. This is probably what you want to use.
             - ``False``: will not apply antialiasing for tensors on any mode. PIL
               images are still antialiased on bilinear or bicubic modes, because
@@ -652,8 +643,8 @@ def resized_crop(
               PIL images. This value exists for legacy reasons and you probably
               don't want to use it unless you really know what you are doing.
 
-            The current default is ``None`` **but will change to** ``True`` **in
-            v0.17** for the PIL and Tensor backends to be consistent.
+            The default value changed from ``None`` to ``True`` in
+            v0.17, for the PIL and Tensor backends to be consistent.
     Returns:
         PIL Image or Tensor: Cropped image.
     """
@@ -1590,28 +1581,3 @@ def elastic_transform(
     if not isinstance(img, torch.Tensor):
         output = to_pil_image(output, mode=img.mode)
     return output
-
-
-# TODO in v0.17: remove this helper and change default of antialias to True everywhere
-def _check_antialias(
-    img: Tensor, antialias: Optional[Union[str, bool]], interpolation: InterpolationMode
-) -> Optional[bool]:
-    if isinstance(antialias, str):  # it should be "warn", but we don't bother checking against that
-        if isinstance(img, Tensor) and (
-            interpolation == InterpolationMode.BILINEAR or interpolation == InterpolationMode.BICUBIC
-        ):
-            warnings.warn(
-                "The default value of the antialias parameter of all the resizing transforms "
-                "(Resize(), RandomResizedCrop(), etc.) "
-                "will change from None to True in v0.17, "
-                "in order to be consistent across the PIL and Tensor backends. "
-                "To suppress this warning, directly pass "
-                "antialias=True (recommended, future default), antialias=None (current default, "
-                "which means False for Tensors and True for PIL), "
-                "or antialias=False (only works on Tensors - PIL will still use antialiasing). "
-                "This also applies if you are using the inference transforms from the models weights: "
-                "update the call to weights.transforms(antialias=True)."
-            )
-        antialias = None
-
-    return antialias

torchvision/transforms/transforms.py

@@ -285,13 +285,6 @@ class Resize(torch.nn.Module):
     If the image is torch Tensor, it is expected
     to have [..., H, W] shape, where ... means a maximum of two leading dimensions
 
-    .. warning::
-        The output image might be different depending on its type: when downsampling, the interpolation of PIL images
-        and tensors is slightly different, because PIL applies antialiasing. This may lead to significant differences
-        in the performance of a network. Therefore, it is preferable to train and serve a model with the same input
-        types. See also below the ``antialias`` parameter, which can help making the output of PIL images and tensors
-        closer.
-
     Args:
         size (sequence or int): Desired output size. If size is a sequence like
             (h, w), output size will be matched to this. If size is an int,
@@ -321,7 +314,7 @@ class Resize(torch.nn.Module):
             tensors), antialiasing makes no sense and this parameter is ignored.
             Possible values are:
 
-            - ``True``: will apply antialiasing for bilinear or bicubic modes.
+            - ``True`` (default): will apply antialiasing for bilinear or bicubic modes.
               Other mode aren't affected. This is probably what you want to use.
             - ``False``: will not apply antialiasing for tensors on any mode. PIL
               images are still antialiased on bilinear or bicubic modes, because
@@ -330,11 +323,11 @@ class Resize(torch.nn.Module):
               PIL images. This value exists for legacy reasons and you probably
               don't want to use it unless you really know what you are doing.
 
-            The current default is ``None`` **but will change to** ``True`` **in
-            v0.17** for the PIL and Tensor backends to be consistent.
+            The default value changed from ``None`` to ``True`` in
+            v0.17, for the PIL and Tensor backends to be consistent.
     """
 
-    def __init__(self, size, interpolation=InterpolationMode.BILINEAR, max_size=None, antialias="warn"):
+    def __init__(self, size, interpolation=InterpolationMode.BILINEAR, max_size=None, antialias=True):
         super().__init__()
         _log_api_usage_once(self)
         if not isinstance(size, (int, Sequence)):
@@ -884,7 +877,7 @@ class RandomResizedCrop(torch.nn.Module):
             tensors), antialiasing makes no sense and this parameter is ignored.
             Possible values are:
 
-            - ``True``: will apply antialiasing for bilinear or bicubic modes.
+            - ``True`` (default): will apply antialiasing for bilinear or bicubic modes.
               Other mode aren't affected. This is probably what you want to use.
             - ``False``: will not apply antialiasing for tensors on any mode. PIL
               images are still antialiased on bilinear or bicubic modes, because
@@ -893,8 +886,8 @@ class RandomResizedCrop(torch.nn.Module):
               PIL images. This value exists for legacy reasons and you probably
               don't want to use it unless you really know what you are doing.
 
-            The current default is ``None`` **but will change to** ``True`` **in
-            v0.17** for the PIL and Tensor backends to be consistent.
+            The default value changed from ``None`` to ``True`` in
+            v0.17, for the PIL and Tensor backends to be consistent.
     """
 
     def __init__(
@@ -903,7 +896,7 @@ class RandomResizedCrop(torch.nn.Module):
         scale=(0.08, 1.0),
         ratio=(3.0 / 4.0, 4.0 / 3.0),
         interpolation=InterpolationMode.BILINEAR,
-        antialias: Optional[Union[str, bool]] = "warn",
+        antialias: Optional[bool] = True,
     ):
         super().__init__()
         _log_api_usage_once(self)

torchvision/transforms/v2/_geometry.py

@@ -10,7 +10,6 @@ from torchvision import transforms as _transforms, tv_tensors
 from torchvision.ops.boxes import box_iou
 from torchvision.transforms.functional import _get_perspective_coeffs
 from torchvision.transforms.v2 import functional as F, InterpolationMode, Transform
-from torchvision.transforms.v2.functional._geometry import _check_interpolation
 from torchvision.transforms.v2.functional._utils import _FillType
 
 from ._transform import _RandomApplyTransform
@@ -81,13 +80,6 @@ class Resize(Transform):
     it can have arbitrary number of leading batch dimensions. For example,
     the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
 
-    .. warning::
-        The output image might be different depending on its type: when downsampling, the interpolation of PIL images
-        and tensors is slightly different, because PIL applies antialiasing. This may lead to significant differences
-        in the performance of a network. Therefore, it is preferable to train and serve a model with the same input
-        types. See also below the ``antialias`` parameter, which can help making the output of PIL images and tensors
-        closer.
-
     Args:
         size (sequence or int): Desired output size. If size is a sequence like
             (h, w), output size will be matched to this. If size is an int,
@@ -117,7 +109,7 @@ class Resize(Transform):
             tensors), antialiasing makes no sense and this parameter is ignored.
             Possible values are:
 
-            - ``True``: will apply antialiasing for bilinear or bicubic modes.
+            - ``True`` (default): will apply antialiasing for bilinear or bicubic modes.
               Other mode aren't affected. This is probably what you want to use.
             - ``False``: will not apply antialiasing for tensors on any mode. PIL
               images are still antialiased on bilinear or bicubic modes, because
@@ -126,8 +118,8 @@ class Resize(Transform):
               PIL images. This value exists for legacy reasons and you probably
               don't want to use it unless you really know what you are doing.
 
-            The current default is ``None`` **but will change to** ``True`` **in
-            v0.17** for the PIL and Tensor backends to be consistent.
+            The default value changed from ``None`` to ``True`` in
+            v0.17, for the PIL and Tensor backends to be consistent.
     """
 
     _v1_transform_cls = _transforms.Resize
@@ -137,7 +129,7 @@ class Resize(Transform):
         size: Union[int, Sequence[int]],
         interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
         max_size: Optional[int] = None,
-        antialias: Optional[Union[str, bool]] = "warn",
+        antialias: Optional[bool] = True,
     ) -> None:
         super().__init__()
 
@@ -151,7 +143,7 @@ class Resize(Transform):
             )
         self.size = size
 
-        self.interpolation = _check_interpolation(interpolation)
+        self.interpolation = interpolation
         self.max_size = max_size
         self.antialias = antialias
 
@@ -231,7 +223,7 @@ class RandomResizedCrop(Transform):
             tensors), antialiasing makes no sense and this parameter is ignored.
             Possible values are:
 
-            - ``True``: will apply antialiasing for bilinear or bicubic modes.
+            - ``True`` (default): will apply antialiasing for bilinear or bicubic modes.
               Other mode aren't affected. This is probably what you want to use.
             - ``False``: will not apply antialiasing for tensors on any mode. PIL
               images are still antialiased on bilinear or bicubic modes, because
@@ -240,8 +232,8 @@ class RandomResizedCrop(Transform):
               PIL images. This value exists for legacy reasons and you probably
               don't want to use it unless you really know what you are doing.
 
-            The current default is ``None`` **but will change to** ``True`` **in
-            v0.17** for the PIL and Tensor backends to be consistent.
+            The default value changed from ``None`` to ``True`` in
+            v0.17, for the PIL and Tensor backends to be consistent.
     """
 
     _v1_transform_cls = _transforms.RandomResizedCrop
@@ -252,7 +244,7 @@ class RandomResizedCrop(Transform):
         scale: Tuple[float, float] = (0.08, 1.0),
         ratio: Tuple[float, float] = (3.0 / 4.0, 4.0 / 3.0),
         interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
-        antialias: Optional[Union[str, bool]] = "warn",
+        antialias: Optional[bool] = True,
     ) -> None:
         super().__init__()
         self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
@@ -268,7 +260,7 @@ class RandomResizedCrop(Transform):
 
         self.scale = scale
         self.ratio = ratio
-        self.interpolation = _check_interpolation(interpolation)
+        self.interpolation = interpolation
         self.antialias = antialias
 
         self._log_ratio = torch.log(torch.tensor(self.ratio))
@@ -622,7 +614,7 @@ class RandomRotation(Transform):
     ) -> None:
         super().__init__()
         self.degrees = _setup_angle(degrees, name="degrees", req_sizes=(2,))
-        self.interpolation = _check_interpolation(interpolation)
+        self.interpolation = interpolation
         self.expand = expand
 
         self.fill = fill
@@ -724,7 +716,7 @@ class RandomAffine(Transform):
         else:
             self.shear = shear
 
-        self.interpolation = _check_interpolation(interpolation)
+        self.interpolation = interpolation
         self.fill = fill
         self._fill = _setup_fill_arg(fill)
 
@@ -969,7 +961,7 @@ class RandomPerspective(_RandomApplyTransform):
             raise ValueError("Argument distortion_scale value should be between 0 and 1")
 
         self.distortion_scale = distortion_scale
-        self.interpolation = _check_interpolation(interpolation)
+        self.interpolation = interpolation
         self.fill = fill
         self._fill = _setup_fill_arg(fill)
 
@@ -1070,7 +1062,7 @@ class ElasticTransform(Transform):
         self.alpha = _setup_number_or_seq(alpha, "alpha")
         self.sigma = _setup_number_or_seq(sigma, "sigma")
 
-        self.interpolation = _check_interpolation(interpolation)
+        self.interpolation = interpolation
         self.fill = fill
         self._fill = _setup_fill_arg(fill)
 
@@ -1263,7 +1255,7 @@ class ScaleJitter(Transform):
             tensors), antialiasing makes no sense and this parameter is ignored.
             Possible values are:
 
-            - ``True``: will apply antialiasing for bilinear or bicubic modes.
+            - ``True`` (default): will apply antialiasing for bilinear or bicubic modes.
               Other mode aren't affected. This is probably what you want to use.
             - ``False``: will not apply antialiasing for tensors on any mode. PIL
               images are still antialiased on bilinear or bicubic modes, because
@@ -1272,8 +1264,8 @@ class ScaleJitter(Transform):
               PIL images. This value exists for legacy reasons and you probably
               don't want to use it unless you really know what you are doing.
 
-            The current default is ``None`` **but will change to** ``True`` **in
-            v0.17** for the PIL and Tensor backends to be consistent.
+            The default value changed from ``None`` to ``True`` in
+            v0.17, for the PIL and Tensor backends to be consistent.
     """
 
     def __init__(
@@ -1281,12 +1273,12 @@ class ScaleJitter(Transform):
         target_size: Tuple[int, int],
         scale_range: Tuple[float, float] = (0.1, 2.0),
         interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
-        antialias: Optional[Union[str, bool]] = "warn",
+        antialias: Optional[bool] = True,
     ):
         super().__init__()
         self.target_size = target_size
         self.scale_range = scale_range
-        self.interpolation = _check_interpolation(interpolation)
+        self.interpolation = interpolation
         self.antialias = antialias
 
     def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
@@ -1330,7 +1322,7 @@ class RandomShortestSize(Transform):
             tensors), antialiasing makes no sense and this parameter is ignored.
             Possible values are:
 
-            - ``True``: will apply antialiasing for bilinear or bicubic modes.
+            - ``True`` (default): will apply antialiasing for bilinear or bicubic modes.
               Other mode aren't affected. This is probably what you want to use.
             - ``False``: will not apply antialiasing for tensors on any mode. PIL
               images are still antialiased on bilinear or bicubic modes, because
@@ -1339,8 +1331,8 @@ class RandomShortestSize(Transform):
               PIL images. This value exists for legacy reasons and you probably
               don't want to use it unless you really know what you are doing.
 
-            The current default is ``None`` **but will change to** ``True`` **in
-            v0.17** for the PIL and Tensor backends to be consistent.
+            The default value changed from ``None`` to ``True`` in
+            v0.17, for the PIL and Tensor backends to be consistent.
     """
 
     def __init__(
@@ -1348,12 +1340,12 @@ class RandomShortestSize(Transform):
         min_size: Union[List[int], Tuple[int], int],
         max_size: Optional[int] = None,
         interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
-        antialias: Optional[Union[str, bool]] = "warn",
+        antialias: Optional[bool] = True,
     ):
         super().__init__()
         self.min_size = [min_size] if isinstance(min_size, int) else list(min_size)
         self.max_size = max_size
-        self.interpolation = _check_interpolation(interpolation)
+        self.interpolation = interpolation
         self.antialias = antialias
 
     def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
@@ -1411,7 +1403,7 @@ class RandomResize(Transform):
             tensors), antialiasing makes no sense and this parameter is ignored.
             Possible values are:
 
-            - ``True``: will apply antialiasing for bilinear or bicubic modes.
+            - ``True`` (default): will apply antialiasing for bilinear or bicubic modes.
               Other mode aren't affected. This is probably what you want to use.
             - ``False``: will not apply antialiasing for tensors on any mode. PIL
               images are still antialiased on bilinear or bicubic modes, because
@@ -1420,8 +1412,8 @@ class RandomResize(Transform):
               PIL images. This value exists for legacy reasons and you probably
               don't want to use it unless you really know what you are doing.
 
-            The current default is ``None`` **but will change to** ``True`` **in
-            v0.17** for the PIL and Tensor backends to be consistent.
+            The default value changed from ``None`` to ``True`` in
+            v0.17, for the PIL and Tensor backends to be consistent.
     """
 
     def __init__(
@@ -1429,12 +1421,12 @@ class RandomResize(Transform):
         min_size: int,
         max_size: int,
         interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
-        antialias: Optional[Union[str, bool]] = "warn",
+        antialias: Optional[bool] = True,
     ) -> None:
         super().__init__()
         self.min_size = min_size
         self.max_size = max_size
-        self.interpolation = _check_interpolation(interpolation)
+        self.interpolation = interpolation
         self.antialias = antialias
 
     def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:

torchvision/transforms/v2/functional/_geometry.py

@@ -11,7 +11,6 @@ from torchvision import tv_tensors
 from torchvision.transforms import _functional_pil as _FP
 from torchvision.transforms._functional_tensor import _pad_symmetric
 from torchvision.transforms.functional import (
-    _check_antialias,
     _compute_resized_output_size as __compute_resized_output_size,
     _get_perspective_coeffs,
     _interpolation_modes_from_int,
@@ -177,7 +176,7 @@ def resize(
     size: List[int],
     interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
     max_size: Optional[int] = None,
-    antialias: Optional[Union[str, bool]] = "warn",
+    antialias: Optional[bool] = True,
 ) -> torch.Tensor:
     """[BETA] See :class:`~torchvision.transforms.v2.Resize` for details."""
     if torch.jit.is_scripting():
@@ -196,17 +195,15 @@ def resize_image(
     size: List[int],
     interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
     max_size: Optional[int] = None,
-    antialias: Optional[Union[str, bool]] = "warn",
+    antialias: Optional[bool] = True,
 ) -> torch.Tensor:
     interpolation = _check_interpolation(interpolation)
-    antialias = _check_antialias(img=image, antialias=antialias, interpolation=interpolation)
-    assert not isinstance(antialias, str)
     antialias = False if antialias is None else antialias
     align_corners: Optional[bool] = None
     if interpolation == InterpolationMode.BILINEAR or interpolation == InterpolationMode.BICUBIC:
         align_corners = False
     else:
-        # The default of antialias should be True from 0.17, so we don't warn or
+        # The default of antialias is True from 0.17, so we don't warn or
         # error if other interpolation modes are used. This is documented.
         antialias = False
 
@@ -297,7 +294,7 @@ def __resize_image_pil_dispatch(
     size: Union[Sequence[int], int],
     interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
     max_size: Optional[int] = None,
-    antialias: Optional[Union[str, bool]] = "warn",
+    antialias: Optional[bool] = True,
 ) -> PIL.Image.Image:
     if antialias is False:
         warnings.warn("Anti-alias option is always applied for PIL Image input. Argument antialias is ignored.")
@@ -361,7 +358,7 @@ def resize_video(
     size: List[int],
     interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
     max_size: Optional[int] = None,
-    antialias: Optional[Union[str, bool]] = "warn",
+    antialias: Optional[bool] = True,
 ) -> torch.Tensor:
     return resize_image(video, size=size, interpolation=interpolation, max_size=max_size, antialias=antialias)
 
@@ -2066,7 +2063,7 @@ def resized_crop(
     width: int,
     size: List[int],
     interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
-    antialias: Optional[Union[str, bool]] = "warn",
+    antialias: Optional[bool] = True,
 ) -> torch.Tensor:
     """[BETA] See :class:`~torchvision.transforms.v2.RandomResizedCrop` for details."""
     if torch.jit.is_scripting():
@@ -2106,7 +2103,7 @@ def resized_crop_image(
     width: int,
     size: List[int],
     interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
-    antialias: Optional[Union[str, bool]] = "warn",
+    antialias: Optional[bool] = True,
 ) -> torch.Tensor:
     image = crop_image(image, top, left, height, width)
     return resize_image(image, size, interpolation=interpolation, antialias=antialias)
@@ -2134,7 +2131,7 @@ def _resized_crop_image_pil_dispatch(
     width: int,
     size: List[int],
     interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
-    antialias: Optional[Union[str, bool]] = "warn",
+    antialias: Optional[bool] = True,
 ) -> PIL.Image.Image:
     if antialias is False:
         warnings.warn("Anti-alias option is always applied for PIL Image input. Argument antialias is ignored.")
@@ -2203,7 +2200,7 @@ def resized_crop_video(
     width: int,
     size: List[int],
     interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
-    antialias: Optional[Union[str, bool]] = "warn",
+    antialias: Optional[bool] = True,
 ) -> torch.Tensor:
     return resized_crop_image(
         video, top, left, height, width, antialias=antialias, size=size, interpolation=interpolation