Update deprecation messages stating APIs will be removed in 0.14 (#5387)

* properly deprecate legacy implementation

* cleanup

* use warning over deprecation directive

* remove patch version

* fix link in Kinetics docstring

* Some more

* fix affine functional tests
Co-authored-by: Nicolas Hug <nicolashug@fb.com>

docs/source/datasets.rst

@@ -128,6 +128,7 @@ Video classification
     :template: class_dataset.rst
 
     HMDB51
+    Kinetics
     Kinetics400
     UCF101
 

test/test_functional_tensor.py

@@ -2,6 +2,7 @@ import colorsys
 import itertools
 import math
 import os
+import re
 from typing import Sequence
 
 import numpy as np
@@ -23,7 +24,6 @@ from common_utils import (
 )
 from torchvision.transforms import InterpolationMode
 
-
 NEAREST, BILINEAR, BICUBIC = InterpolationMode.NEAREST, InterpolationMode.BILINEAR, InterpolationMode.BICUBIC
 
 
@@ -141,7 +141,13 @@ class TestRotate:
     def test_rotate_deprecation_resample(self):
         tensor, _ = _create_data(26, 26)
         # assert deprecation warning and non-BC
-        with pytest.warns(UserWarning, match=r"Argument resample is deprecated and will be removed"):
+        with pytest.warns(
+            UserWarning,
+            match=re.escape(
+                "The parameter 'resample' is deprecated since 0.12 and will be removed 0.14. "
+                "Please use 'interpolation' instead."
+            ),
+        ):
             res1 = F.rotate(tensor, 45, resample=2)
             res2 = F.rotate(tensor, 45, interpolation=BILINEAR)
             assert_equal(res1, res2)
@@ -365,7 +371,13 @@ class TestAffine:
         tensor, pil_img = _create_data(26, 26, device=device)
 
         # assert deprecation warning and non-BC
-        with pytest.warns(UserWarning, match=r"Argument resample is deprecated and will be removed"):
+        with pytest.warns(
+            UserWarning,
+            match=re.escape(
+                "The parameter 'resample' is deprecated since 0.12 and will be removed in 0.14. "
+                "Please use 'interpolation' instead."
+            ),
+        ):
             res1 = F.affine(tensor, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=2)
             res2 = F.affine(tensor, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=BILINEAR)
             assert_equal(res1, res2)
@@ -376,7 +388,13 @@ class TestAffine:
             res2 = F.affine(tensor, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=BILINEAR)
             assert_equal(res1, res2)
 
-        with pytest.warns(UserWarning, match=r"Argument fillcolor is deprecated and will be removed"):
+        with pytest.warns(
+            UserWarning,
+            match=re.escape(
+                "The parameter 'fillcolor' is deprecated since 0.12 and will be removed in 0.14. "
+                "Please use 'fill' instead."
+            ),
+        ):
             res1 = F.affine(pil_img, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], fillcolor=10)
             res2 = F.affine(pil_img, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], fill=10)
             # we convert the PIL images to numpy as assert_equal doesn't work on PIL images.

test/test_transforms.py

 import math
 import os
 import random
+import re
 from functools import partial
 
 import numpy as np
@@ -1828,7 +1829,13 @@ def test_random_rotation():
     t.__repr__()
 
     # assert deprecation warning and non-BC
-    with pytest.warns(UserWarning, match=r"Argument resample is deprecated and will be removed"):
+    with pytest.warns(
+        UserWarning,
+        match=re.escape(
+            "The parameter 'resample' is deprecated since 0.12 and will be removed 0.14. "
+            "Please use 'interpolation' instead."
+        ),
+    ):
         t = transforms.RandomRotation((-10, 10), resample=2)
         assert t.interpolation == transforms.InterpolationMode.BILINEAR
 
@@ -2167,11 +2174,23 @@ def test_random_affine():
     assert "bilinear" in t.__repr__()
 
     # assert deprecation warning and non-BC
-    with pytest.warns(UserWarning, match=r"Argument resample is deprecated and will be removed"):
+    with pytest.warns(
+        UserWarning,
+        match=re.escape(
+            "The parameter 'resample' is deprecated since 0.12 and will be removed in 0.14. "
+            "Please use 'interpolation' instead."
+        ),
+    ):
         t = transforms.RandomAffine(10, resample=2)
         assert t.interpolation == transforms.InterpolationMode.BILINEAR
 
-    with pytest.warns(UserWarning, match=r"Argument fillcolor is deprecated and will be removed"):
+    with pytest.warns(
+        UserWarning,
+        match=re.escape(
+            "The parameter 'fillcolor' is deprecated since 0.12 and will be removed in 0.14. "
+            "Please use 'fill' instead."
+        ),
+    ):
         t = transforms.RandomAffine(10, fillcolor=10)
         assert t.fill == 10
 

torchvision/csrc/models/modelsimpl.h

@@ -36,8 +36,8 @@ inline bool double_compare(double a, double b) {
 
 inline void deprecation_warning() {
   TORCH_WARN_ONCE(
-      "The vision::models namespace is not actively maintained, use at "
-      "your own discretion. We recommend using Torch Script instead: "
+      "The vision::models namespace is deprecated since 0.12 and will be "
+      "removed in 0.14. We recommend using Torch Script instead: "
       "https://pytorch.org/tutorials/advanced/cpp_export.html");
 }
 

torchvision/datasets/kinetics.py

@@ -20,7 +20,7 @@ def _dl_wrap(tarpath: str, videopath: str, line: str) -> None:
 
 
 class Kinetics(VisionDataset):
-    """` Generic Kinetics <https://deepmind.com/research/open-source/open-source-datasets/kinetics/>`_
+    """`Generic Kinetics <https://deepmind.com/research/open-source/open-source-datasets/kinetics/>`_
     dataset.
 
     Kinetics-400/600/700 are action recognition video datasets.
@@ -49,6 +49,7 @@ class Kinetics(VisionDataset):
                 │   ├──  class2
                 │   │   ├──   clipx.mp4
                 │   │    └── ...
+
             Note: split is appended automatically using the split argument.
         frames_per_clip (int): number of frames in a clip
         num_classes (int): select between Kinetics-400 (default), Kinetics-600, and Kinetics-700
@@ -247,6 +248,10 @@ class Kinetics400(Kinetics):
     `Kinetics-400 <https://deepmind.com/research/open-source/open-source-datasets/kinetics/>`_
     dataset.
 
+    .. warning::
+        This class was deprecated in ``0.12`` and will be removed in ``0.14``. Please use
+        ``Kinetics(..., num_classes='400')`` instead.
+
     Kinetics-400 is an action recognition video dataset.
     This dataset consider every video as a collection of video clips of fixed size, specified
     by ``frames_per_clip``, where the step in frames between each clip is given by
@@ -300,8 +305,8 @@ class Kinetics400(Kinetics):
         **kwargs: Any,
     ) -> None:
         warnings.warn(
-            "Kinetics400 is deprecated and will be removed in a future release."
-            'It was replaced by Kinetics(..., num_classes="400").'
+            "The Kinetics400 class is deprecated since 0.12 and will be removed in 0.14."
+            "Please use Kinetics(..., num_classes='400') instead."
         )
         if any(value is not None for value in (num_classes, split, download, num_download_workers)):
             raise RuntimeError(

torchvision/models/mobilenetv2.py

@@ -23,7 +23,7 @@ model_urls = {
 class _DeprecatedConvBNAct(ConvNormActivation):
     def __init__(self, *args, **kwargs):
         warnings.warn(
-            "The ConvBNReLU/ConvBNActivation classes are deprecated and will be removed in future versions. "
+            "The ConvBNReLU/ConvBNActivation classes are deprecated since 0.12 and will be removed in 0.14. "
             "Use torchvision.ops.misc.ConvNormActivation instead.",
             FutureWarning,
         )

torchvision/models/segmentation/segmentation.py

@@ -5,6 +5,6 @@ from . import *  # noqa: F401, F403
 
 
 warnings.warn(
-    "The 'torchvision.models.segmentation.segmentation' module is deprecated. Please use directly the parent module "
-    "instead."
+    "The 'torchvision.models.segmentation.segmentation' module is deprecated since 0.12 and will be removed in "
+    "0.14. Please use the 'torchvision.models.segmentation' directly instead."
 )

torchvision/ops/poolers.py

@@ -288,13 +288,11 @@ class MultiScaleRoIAlign(nn.Module):
         self.canonical_level = canonical_level
 
     def convert_to_roi_format(self, boxes: List[Tensor]) -> Tensor:
-        # TODO: deprecate eventually
-        warnings.warn("`convert_to_roi_format` will no loger be public in future releases.", FutureWarning)
+        warnings.warn("The 'convert_to_roi_format' method is deprecated since 0.12 and will be removed in 0.14.")
         return _convert_to_roi_format(boxes)
 
     def infer_scale(self, feature: Tensor, original_size: List[int]) -> float:
-        # TODO: deprecate eventually
-        warnings.warn("`infer_scale` will no loger be public in future releases.", FutureWarning)
+        warnings.warn("The 'infer_scale' method is deprecated since 0.12 and will be removed in 0.14.")
         return _infer_scale(feature, original_size)
 
     def setup_setup_scales(
@@ -302,8 +300,7 @@ class MultiScaleRoIAlign(nn.Module):
         features: List[Tensor],
         image_shapes: List[Tuple[int, int]],
     ) -> None:
-        # TODO: deprecate eventually
-        warnings.warn("`setup_setup_scales` will no loger be public in future releases.", FutureWarning)
+        warnings.warn("The 'setup_setup_scales' method is deprecated since 0.12 and will be removed in 0.14.")
         self.scales, self.map_levels = _setup_scales(features, image_shapes, self.canonical_scale, self.canonical_level)
 
     def forward(

torchvision/transforms/_functional_video.py

@@ -3,7 +3,10 @@ import warnings
 import torch
 
 
-warnings.warn("The _functional_video module is deprecated. Please use the functional module instead.")
+warnings.warn(
+    "The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in 0.14. "
+    "Please use the 'torchvision.transforms.functional' module instead."
+)
 
 
 def _is_tensor_video_clip(clip):

torchvision/transforms/_transforms_video.py

@@ -22,7 +22,10 @@ __all__ = [
 ]
 
 
-warnings.warn("The _transforms_video module is deprecated. Please use the transforms module instead.")
+warnings.warn(
+    "The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in 0.14. "
+    "Please use the 'torchvision.transforms' module instead."
+)
 
 
 class RandomCropVideo(RandomCrop):

torchvision/transforms/functional.py

@@ -1025,6 +1025,10 @@ def rotate(
             .. note::
                 In torchscript mode single int/float value is not supported, please use a sequence
                 of length 1: ``[value, ]``.
+        resample (int, optional):
+            .. warning::
+                This parameter was deprecated in ``0.12`` and will be removed in ``0.14``. Please use ``interpolation``
+                instead.
 
     Returns:
         PIL Image or Tensor: Rotated image.
@@ -1036,7 +1040,8 @@ def rotate(
         _log_api_usage_once(rotate)
     if resample is not None:
         warnings.warn(
-            "Argument resample is deprecated and will be removed since v0.10.0. Please, use interpolation instead"
+            "The parameter 'resample' is deprecated since 0.12 and will be removed 0.14. "
+            "Please use 'interpolation' instead."
         )
         interpolation = _interpolation_modes_from_int(resample)
 
@@ -1107,10 +1112,13 @@ def affine(
             .. note::
                 In torchscript mode single int/float value is not supported, please use a sequence
                 of length 1: ``[value, ]``.
-        fillcolor (sequence, int, float): deprecated argument and will be removed since v0.10.0.
-            Please use the ``fill`` parameter instead.
-        resample (int, optional): deprecated argument and will be removed since v0.10.0.
-            Please use the ``interpolation`` parameter instead.
+        fillcolor (sequence or number, optional):
+            .. warning::
+                This parameter was deprecated in ``0.12`` and will be removed in ``0.14``. Please use ``fill`` instead.
+        resample (int, optional):
+            .. warning::
+                This parameter was deprecated in ``0.12`` and will be removed in ``0.14``. Please use ``interpolation``
+                instead.
         center (sequence, optional): Optional center of rotation. Origin is the upper left corner.
             Default is the center of the image.
 
@@ -1121,7 +1129,8 @@ def affine(
         _log_api_usage_once(affine)
     if resample is not None:
         warnings.warn(
-            "Argument resample is deprecated and will be removed since v0.10.0. Please, use interpolation instead"
+            "The parameter 'resample' is deprecated since 0.12 and will be removed in 0.14. "
+            "Please use 'interpolation' instead."
         )
         interpolation = _interpolation_modes_from_int(resample)
 
@@ -1134,7 +1143,10 @@ def affine(
         interpolation = _interpolation_modes_from_int(interpolation)
 
     if fillcolor is not None:
-        warnings.warn("Argument fillcolor is deprecated and will be removed since v0.10.0. Please, use fill instead")
+        warnings.warn(
+            "The parameter 'fillcolor' is deprecated since 0.12 and will be removed in 0.14. "
+            "Please use 'fill' instead."
+        )
         fill = fillcolor
 
     if not isinstance(angle, (int, float)):

torchvision/transforms/transforms.py

@@ -1272,8 +1272,10 @@ class RandomRotation(torch.nn.Module):
             Default is the center of the image.
         fill (sequence or number): Pixel fill value for the area outside the rotated
             image. Default is ``0``. If given a number, the value is used for all bands respectively.
-        resample (int, optional): deprecated argument and will be removed since v0.10.0.
-            Please use the ``interpolation`` parameter instead.
+        resample (int, optional):
+            .. warning::
+                This parameter was deprecated in ``0.12`` and will be removed in ``0.14``. Please use ``interpolation``
+                instead.
 
     .. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters
 
@@ -1286,7 +1288,8 @@ class RandomRotation(torch.nn.Module):
         _log_api_usage_once(self)
         if resample is not None:
             warnings.warn(
-                "Argument resample is deprecated and will be removed since v0.10.0. Please, use interpolation instead"
+                "The parameter 'resample' is deprecated since 0.12 and will be removed 0.14. "
+                "Please use 'interpolation' instead."
             )
             interpolation = _interpolation_modes_from_int(resample)
 
@@ -1383,10 +1386,13 @@ class RandomAffine(torch.nn.Module):
             For backward compatibility integer values (e.g. ``PIL.Image.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): deprecated argument and will be removed since v0.10.0.
-            Please use the ``fill`` parameter instead.
-        resample (int, optional): deprecated argument and will be removed since v0.10.0.
-            Please use the ``interpolation`` parameter instead.
+        fillcolor (sequence or number, optional):
+            .. warning::
+                This parameter was deprecated in ``0.12`` and will be removed in ``0.14``. Please use ``fill`` instead.
+        resample (int, optional):
+            .. warning::
+                This parameter was deprecated in ``0.12`` and will be removed in ``0.14``. Please use ``interpolation``
+                instead.
         center (sequence, optional): Optional center of rotation, (x, y). Origin is the upper left corner.
             Default is the center of the image.
 
@@ -1410,7 +1416,8 @@ class RandomAffine(torch.nn.Module):
         _log_api_usage_once(self)
         if resample is not None:
             warnings.warn(
-                "Argument resample is deprecated and will be removed since v0.10.0. Please, use interpolation instead"
+                "The parameter 'resample' is deprecated since 0.12 and will be removed in 0.14. "
+                "Please use 'interpolation' instead."
             )
             interpolation = _interpolation_modes_from_int(resample)
 
@@ -1424,7 +1431,8 @@ class RandomAffine(torch.nn.Module):
 
         if fillcolor is not None:
             warnings.warn(
-                "Argument fillcolor is deprecated and will be removed since v0.10.0. Please, use fill instead"
+                "The parameter 'fillcolor' is deprecated since 0.12 and will be removed in 0.14. "
+                "Please use 'fill' instead."
             )
             fill = fillcolor
 

torchvision/utils.py

@@ -43,6 +43,10 @@ def make_grid(
         value_range (tuple, optional): tuple (min, max) where min and max are numbers,
             then these numbers are used to normalize the image. By default, min and max
             are computed from the tensor.
+        range (tuple. optional):
+            .. warning::
+                This parameter was deprecated in ``0.12`` and will be removed in ``0.14``. Please use ``value_range``
+                instead.
         scale_each (bool, optional): If ``True``, scale each image in the batch of
             images separately rather than the (min, max) over all images. Default: ``False``.
         pad_value (float, optional): Value for the padded pixels. Default: ``0``.
@@ -56,8 +60,10 @@ def make_grid(
         raise TypeError(f"tensor or list of tensors expected, got {type(tensor)}")
 
     if "range" in kwargs.keys():
-        warning = "range will be deprecated, please use value_range instead."
-        warnings.warn(warning)
+        warnings.warn(
+            "The parameter 'range' is deprecated since 0.12 and will be removed in 0.14. "
+            "Please use 'value_range' instead."
+        )
         value_range = kwargs["range"]
 
     # if list of tensors, convert to a 4D mini-batch Tensor