Change antialias default from None to True

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 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 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

@@ -1,6 +1,5 @@
 import os
 import sys
-import warnings
 
 import numpy as np
 import PIL.Image
@@ -428,22 +427,6 @@ def test_resized_crop_save_load(self, tmpdir):
         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 pathlib
 import pickle
 import random
-import warnings
 
 import numpy as np
 
@@ -726,46 +725,6 @@ def test__transform(self, inpt):
         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.transforms.v2 as transforms
 from common_utils import (
     assert_equal,
-    assert_no_warnings,
     cache,
     cpu_and_cuda,
     freeze_rng_state,
@@ -350,12 +349,6 @@ def adapt_fill(value, *, dtype):
 ]
 
 
-@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 @@ def test_max_size_error(self, size, make_input):
         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 @@ def __init__(
         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 @@ def __init__(
         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 @@
     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 @@
             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 @@
               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 @@
     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 @@
     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 @@
             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 @@
               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 @@
     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