Remove deprecated APIs for 0.14 (#6258)

* Remove Kinetics400 class

* Remove '2007-test' in VOC

* Remove some MobileNet layer classes

* Remove torchvision/models/segmentation/segmentation.py

* Remove some MultiScaleRoIAlign methods

* Remove torchvision/transforms/_functional_video.py

* Remove torchvision/transforms/_transforms_video.py

* Remove resample parameter in transforms

* Remove 'range' parameter

* Remove 'fill_value' parameter in transforms

* Revert to original warning for C++ models - looks like we should still keep them around?

* pre-commit

* Fix docs

* Remove test/test_transforms_video.py

* Some fixes

* Remove more tests

* Revert changes to C++ models

* Add back _transforms_video and change warning message

* Change back the warning message, and will change the warning message on separate PR
Co-authored-by: YosuaMichael <yosuamichaelm@gmail.com>
Co-authored-by: Yosua Michael Maranatha <yosuamichael@fb.com>

docs/source/datasets.rst

@@ -147,7 +147,6 @@ Video classification
 
     HMDB51
     Kinetics
-    Kinetics400
     UCF101
 
 

test/test_datasets.py

@@ -620,11 +620,7 @@ class VOCSegmentationTestCase(datasets_utils.ImageDatasetTestCase):
     )
 
     def inject_fake_data(self, tmpdir, config):
-        year, is_test_set = (
-            ("2007", True)
-            if config["year"] == "2007-test" or config["image_set"] == "test"
-            else (config["year"], False)
-        )
+        year, is_test_set = config["year"], config["image_set"] == "test"
         image_set = config["image_set"]
 
         base_dir = pathlib.Path(tmpdir)
@@ -965,25 +961,6 @@ class KineticsTestCase(datasets_utils.VideoDatasetTestCase):
         return num_videos_per_class * len(classes)
 
 
-class Kinetics400TestCase(datasets_utils.VideoDatasetTestCase):
-    DATASET_CLASS = datasets.Kinetics400
-
-    def inject_fake_data(self, tmpdir, config):
-        classes = ("Abseiling", "Zumba")
-        num_videos_per_class = 2
-
-        digits = string.ascii_letters + string.digits + "-_"
-        for cls in classes:
-            datasets_utils.create_video_folder(
-                tmpdir,
-                cls,
-                lambda _: f"{datasets_utils.create_random_string(11, digits)}.avi",
-                num_videos_per_class,
-            )
-
-        return num_videos_per_class * len(classes)
-
-
 class HMDB51TestCase(datasets_utils.VideoDatasetTestCase):
     DATASET_CLASS = datasets.HMDB51
 

test/test_datasets_download.py

@@ -245,6 +245,7 @@ def cifar100():
 
 
 def voc():
+    # TODO: Also test the "2007-test" key
     return itertools.chain(
         *[
             collect_download_configs(
@@ -252,7 +253,7 @@ def voc():
                 name=f"VOC, {year}",
                 file="voc",
             )
-            for year in ("2007", "2007-test", "2008", "2009", "2010", "2011", "2012")
+            for year in ("2007", "2008", "2009", "2010", "2011", "2012")
         ]
     )
 

test/test_functional_tensor.py

@@ -139,20 +139,6 @@ class TestRotate:
         center = (20, 22)
         _test_fn_on_batch(batch_tensors, F.rotate, angle=32, interpolation=NEAREST, expand=True, center=center)
 
-    def test_rotate_deprecation_resample(self):
-        tensor, _ = _create_data(26, 26)
-        # assert deprecation warning and non-BC
-        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)
-
     def test_rotate_interpolation_type(self):
         tensor, _ = _create_data(26, 26)
         # assert changed type warning
@@ -377,18 +363,6 @@ class TestAffine:
     def test_warnings(self, device):
         tensor, pil_img = _create_data(26, 26, device=device)
 
-        # assert deprecation warning and non-BC
-        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)
-
         # assert changed type warning
         with pytest.warns(
             UserWarning,
@@ -401,18 +375,6 @@ 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=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.
-            assert_equal(np.asarray(res1), np.asarray(res2))
-
 
 def _get_data_dims_and_points_for_perspective():
     # Ideally we would parametrize independently over data dims and points, but

test/test_transforms.py

@@ -1866,17 +1866,6 @@ def test_random_rotation():
     # Checking if RandomRotation can be printed as string
     t.__repr__()
 
-    # assert deprecation warning and non-BC
-    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
-
     # assert changed type warning
     with pytest.warns(
         UserWarning,
@@ -2217,27 +2206,6 @@ def test_random_affine():
     t = transforms.RandomAffine(10, interpolation=transforms.InterpolationMode.BILINEAR)
     assert "bilinear" in t.__repr__()
 
-    # assert deprecation warning and non-BC
-    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=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
-
     # assert changed type warning
     with pytest.warns(
         UserWarning,

torchvision/datasets/__init__.py

@@ -31,7 +31,7 @@ from .gtsrb import GTSRB
 from .hmdb51 import HMDB51
 from .imagenet import ImageNet
 from .inaturalist import INaturalist
-from .kinetics import Kinetics, Kinetics400
+from .kinetics import Kinetics
 from .kitti import Kitti
 from .lfw import LFWPairs, LFWPeople
 from .lsun import LSUN, LSUNClass
@@ -92,7 +92,6 @@ __all__ = (
     "SBDataset",
     "VisionDataset",
     "USPS",
-    "Kinetics400",
     "Kinetics",
     "HMDB51",
     "UCF101",

torchvision/datasets/kinetics.py

@@ -2,7 +2,6 @@ import csv
 import os
 import time
 import urllib
-import warnings
 from functools import partial
 from multiprocessing import Pool
 from os import path
@@ -246,83 +245,3 @@ class Kinetics(VisionDataset):
             video = self.transform(video)
 
         return video, audio, label
-
-
-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
-    ``step_between_clips``.
-
-    To give an example, for 2 videos with 10 and 15 frames respectively, if ``frames_per_clip=5``
-    and ``step_between_clips=5``, the dataset size will be (2 + 3) = 5, where the first two
-    elements will come from video 1, and the next three elements from video 2.
-    Note that we drop clips which do not have exactly ``frames_per_clip`` elements, so not all
-    frames in a video might be present.
-
-    Internally, it uses a VideoClips object to handle clip creation.
-
-    Args:
-        root (string): Root directory of the Kinetics-400 Dataset. Should be structured as follows:
-
-            .. code::
-
-                root/
-                ├── class1
-                │   ├── clip1.avi
-                │   ├── clip2.avi
-                │   ├── clip3.mp4
-                │   └── ...
-                └── class2
-                    ├── clipx.avi
-                    └── ...
-
-        frames_per_clip (int): number of frames in a clip
-        step_between_clips (int): number of frames between each clip
-        transform (callable, optional): A function/transform that  takes in a TxHxWxC video
-            and returns a transformed version.
-
-    Returns:
-        tuple: A 3-tuple with the following entries:
-
-            - video (Tensor[T, H, W, C]): the `T` video frames
-            - audio(Tensor[K, L]): the audio frames, where `K` is the number of channels
-              and `L` is the number of points
-            - label (int): class of the video clip
-    """
-
-    def __init__(
-        self,
-        root: str,
-        frames_per_clip: int,
-        num_classes: Any = None,
-        split: Any = None,
-        download: Any = None,
-        num_download_workers: Any = None,
-        **kwargs: Any,
-    ) -> None:
-        warnings.warn(
-            "The Kinetics400 class is deprecated since 0.12 and will be removed in 0.14."
-            "Please use Kinetics(..., num_classes='400') instead."
-            "Note that Kinetics(..., num_classes='400') returns video in a Tensor[T, C, H, W] format."
-        )
-        if any(value is not None for value in (num_classes, split, download, num_download_workers)):
-            raise RuntimeError(
-                "Usage of 'num_classes', 'split', 'download', or 'num_download_workers' is not supported in "
-                "Kinetics400. Please use Kinetics instead."
-            )
-
-        super().__init__(
-            root=root,
-            frames_per_clip=frames_per_clip,
-            _legacy=True,
-            **kwargs,
-        )

torchvision/datasets/voc.py

@@ -8,7 +8,6 @@ try:
     from defusedxml.ElementTree import parse as ET_parse
 except ImportError:
     from xml.etree.ElementTree import parse as ET_parse
-import warnings
 from typing import Any, Callable, Dict, List, Optional, Tuple
 
 from PIL import Image
@@ -77,20 +76,8 @@ class _VOCBase(VisionDataset):
         transforms: Optional[Callable] = None,
     ):
         super().__init__(root, transforms, transform, target_transform)
-        if year == "2007-test":
-            if image_set == "test":
-                warnings.warn(
-                    "Accessing the test image set of the year 2007 with year='2007-test' is deprecated "
-                    "since 0.12 and will be removed in 0.14. "
-                    "Please use the combination year='2007' and image_set='test' instead."
-                )
-                year = "2007"
-            else:
-                raise ValueError(
-                    "In the test image set of the year 2007 only image_set='test' is allowed. "
-                    "For all other image sets use year='2007' instead."
-                )
-        self.year = year
+
+        self.year = verify_str_arg(year, "year", valid_values=[str(yr) for yr in range(2007, 2013)])
 
         valid_image_sets = ["train", "trainval", "val"]
         if year == "2007":

torchvision/models/mobilenetv2.py

-import warnings
 from functools import partial
 from typing import Any, Callable, List, Optional
 
@@ -17,24 +16,6 @@ __all__ = ["MobileNetV2", "MobileNet_V2_Weights", "mobilenet_v2"]
 
 
 # necessary for backwards compatibility
-class _DeprecatedConvBNAct(Conv2dNormActivation):
-    def __init__(self, *args, **kwargs):
-        warnings.warn(
-            "The ConvBNReLU/ConvBNActivation classes are deprecated since 0.12 and will be removed in 0.14. "
-            "Use torchvision.ops.misc.Conv2dNormActivation instead.",
-            FutureWarning,
-        )
-        if kwargs.get("norm_layer", None) is None:
-            kwargs["norm_layer"] = nn.BatchNorm2d
-        if kwargs.get("activation_layer", None) is None:
-            kwargs["activation_layer"] = nn.ReLU6
-        super().__init__(*args, **kwargs)
-
-
-ConvBNReLU = _DeprecatedConvBNAct
-ConvBNActivation = _DeprecatedConvBNAct
-
-
 class InvertedResidual(nn.Module):
     def __init__(
         self, inp: int, oup: int, stride: int, expand_ratio: int, norm_layer: Optional[Callable[..., nn.Module]] = None

torchvision/models/mobilenetv3.py

-import warnings
 from functools import partial
 from typing import Any, Callable, List, Optional, Sequence
 
@@ -22,21 +21,6 @@ __all__ = [
 ]
 
 
-class SqueezeExcitation(SElayer):
-    """DEPRECATED"""
-
-    def __init__(self, input_channels: int, squeeze_factor: int = 4):
-        squeeze_channels = _make_divisible(input_channels // squeeze_factor, 8)
-        super().__init__(input_channels, squeeze_channels, scale_activation=nn.Hardsigmoid)
-        self.relu = self.activation
-        delattr(self, "activation")
-        warnings.warn(
-            "This SqueezeExcitation class is deprecated since 0.12 and will be removed in 0.14. "
-            "Use torchvision.ops.SqueezeExcitation instead.",
-            FutureWarning,
-        )
-
-
 class InvertedResidualConfig:
     # Stores information listed at Tables 1 and 2 of the MobileNetV3 paper
     def __init__(

torchvision/models/segmentation/segmentation.py

-import warnings
-
-# Import all methods/classes for BC:
-from . import *  # noqa: F401, F403
-
-
-warnings.warn(
-    "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

-import warnings
 from typing import Dict, List, Optional, Tuple, Union
 
 import torch
@@ -287,22 +286,6 @@ class MultiScaleRoIAlign(nn.Module):
         self.canonical_scale = canonical_scale
         self.canonical_level = canonical_level
 
-    def convert_to_roi_format(self, boxes: List[Tensor]) -> Tensor:
-        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:
-        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(
-        self,
-        features: List[Tensor],
-        image_shapes: List[Tuple[int, int]],
-    ) -> None:
-        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(
         self,
         x: Dict[str, Tensor],

torchvision/transforms/functional.py

@@ -1051,7 +1051,6 @@ def rotate(
     expand: bool = False,
     center: Optional[List[int]] = None,
     fill: Optional[List[float]] = None,
-    resample: Optional[int] = None,
 ) -> Tensor:
     """Rotate the image by angle.
     If the image is torch Tensor, it is expected
@@ -1077,11 +1076,6 @@ 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.
 
@@ -1090,12 +1084,6 @@ def rotate(
     """
     if not torch.jit.is_scripting() and not torch.jit.is_tracing():
         _log_api_usage_once(rotate)
-    if resample is not None:
-        warnings.warn(
-            "The parameter 'resample' is deprecated since 0.12 and will be removed 0.14. "
-            "Please use 'interpolation' instead."
-        )
-        interpolation = _interpolation_modes_from_int(resample)
 
     # Backward compatibility with integer value
     if isinstance(interpolation, int):
@@ -1138,8 +1126,6 @@ def affine(
     shear: List[float],
     interpolation: InterpolationMode = InterpolationMode.NEAREST,
     fill: Optional[List[float]] = None,
-    resample: Optional[int] = None,
-    fillcolor: Optional[List[float]] = None,
     center: Optional[List[int]] = None,
 ) -> Tensor:
     """Apply affine transformation on the image keeping image center invariant.
@@ -1165,13 +1151,6 @@ def affine(
             .. note::
                 In torchscript mode single int/float value is not supported, please use a sequence
                 of length 1: ``[value, ]``.
-        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.
 
@@ -1180,12 +1159,6 @@ def affine(
     """
     if not torch.jit.is_scripting() and not torch.jit.is_tracing():
         _log_api_usage_once(affine)
-    if resample is not None:
-        warnings.warn(
-            "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)
 
     # Backward compatibility with integer value
     if isinstance(interpolation, int):
@@ -1195,13 +1168,6 @@ def affine(
         )
         interpolation = _interpolation_modes_from_int(interpolation)
 
-    if fillcolor is not None:
-        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)):
         raise TypeError("Argument angle should be int or float")
 

torchvision/transforms/transforms.py

@@ -1289,26 +1289,14 @@ 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):
-            .. 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
 
     """
 
-    def __init__(
-        self, degrees, interpolation=InterpolationMode.NEAREST, expand=False, center=None, fill=0, resample=None
-    ):
+    def __init__(self, degrees, interpolation=InterpolationMode.NEAREST, expand=False, center=None, fill=0):
         super().__init__()
         _log_api_usage_once(self)
-        if resample is not None:
-            warnings.warn(
-                "The parameter 'resample' is deprecated since 0.12 and will be removed 0.14. "
-                "Please use 'interpolation' instead."
-            )
-            interpolation = _interpolation_modes_from_int(resample)
 
         # Backward compatibility with integer value
         if isinstance(interpolation, int):
@@ -1325,7 +1313,7 @@ class RandomRotation(torch.nn.Module):
 
         self.center = center
 
-        self.resample = self.interpolation = interpolation
+        self.interpolation = interpolation
         self.expand = expand
 
         if fill is None:
@@ -1362,7 +1350,7 @@ class RandomRotation(torch.nn.Module):
                 fill = [float(f) for f in fill]
         angle = self.get_params(self.degrees)
 
-        return F.rotate(img, angle, self.resample, self.expand, self.center, fill)
+        return F.rotate(img, angle, self.interpolation, self.expand, self.center, fill)
 
     def __repr__(self) -> str:
         interpolate_str = self.interpolation.value
@@ -1405,13 +1393,6 @@ class RandomAffine(torch.nn.Module):
             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):
-            .. 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.
 
@@ -1427,18 +1408,10 @@ class RandomAffine(torch.nn.Module):
         shear=None,
         interpolation=InterpolationMode.NEAREST,
         fill=0,
-        fillcolor=None,
-        resample=None,
         center=None,
     ):
         super().__init__()
         _log_api_usage_once(self)
-        if resample is not None:
-            warnings.warn(
-                "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)
 
         # Backward compatibility with integer value
         if isinstance(interpolation, int):
@@ -1448,13 +1421,6 @@ class RandomAffine(torch.nn.Module):
             )
             interpolation = _interpolation_modes_from_int(interpolation)
 
-        if fillcolor is not None:
-            warnings.warn(
-                "The parameter 'fillcolor' is deprecated since 0.12 and will be removed in 0.14. "
-                "Please use 'fill' instead."
-            )
-            fill = fillcolor
-
         self.degrees = _setup_angle(degrees, name="degrees", req_sizes=(2,))
 
         if translate is not None:
@@ -1476,14 +1442,14 @@ class RandomAffine(torch.nn.Module):
         else:
             self.shear = shear
 
-        self.resample = self.interpolation = interpolation
+        self.interpolation = interpolation
 
         if fill is None:
             fill = 0
         elif not isinstance(fill, (Sequence, numbers.Number)):
             raise TypeError("Fill should be either a sequence or a number.")
 
-        self.fillcolor = self.fill = fill
+        self.fill = fill
 
         if center is not None:
             _check_sequence_input(center, "center", req_sizes=(2,))

torchvision/utils.py

@@ -45,10 +45,6 @@ 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``.
@@ -66,13 +62,6 @@ def make_grid(
         else:
             raise TypeError(f"tensor or list of tensors expected, got {type(tensor)}")
 
-    if "range" in kwargs.keys():
-        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
     if isinstance(tensor, list):
         tensor = torch.stack(tensor, dim=0)