Remove deprecated APIs for 0.14

test/test_datasets.py

@@ -616,15 +616,10 @@ class VOCSegmentationTestCase(datasets_utils.ImageDatasetTestCase):
             year=[f"20{year:02d}" for year in range(7, 13)], image_set=("train", "val", "trainval")
         ),
         dict(year="2007", image_set="test"),
-        dict(year="2007-test", image_set="test"),
     )
 
     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 +960,6 @@ def inject_fake_data(self, tmpdir, config):
         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

@@ -245,14 +245,15 @@ def cifar100():
 
 
 def voc():
+    # TODO: Also test the "2007-test" key
     return itertools.chain(
         *[
             collect_download_configs(
                 lambda: datasets.VOCSegmentation(ROOT, year=year, download=True),
                 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")
         ]
     )
 

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 deprecated since 0.12 and will be "
-      "removed in 0.14. We recommend using Torch Script instead: "
+      "The vision::models namespace is not actively maintained, use at "
+      "your own discretion. We recommend using Torch Script instead: "
       "https://pytorch.org/tutorials/advanced/cpp_export.html");
 }
 

torchvision/datasets/__init__.py

@@ -19,7 +19,7 @@
 from .hmdb51 import HMDB51
 from .imagenet import ImageNet
 from .inaturalist import INaturalist
-from .kinetics import Kinetics400, Kinetics
+from .kinetics import Kinetics
 from .kitti import Kitti
 from .lfw import LFWPeople, LFWPairs
 from .lsun import LSUN, LSUNClass
@@ -80,7 +80,6 @@
     "SBDataset",
     "VisionDataset",
     "USPS",
-    "Kinetics400",
     "Kinetics",
     "HMDB51",
     "UCF101",

torchvision/datasets/kinetics.py

@@ -2,7 +2,6 @@
 import os
 import time
 import urllib
-import warnings
 from functools import partial
 from multiprocessing import Pool
 from os import path
@@ -246,83 +245,3 @@ def __getitem__(self, idx: int) -> Tuple[Tensor, Tensor, int]:
             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 @@
     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, Optional, Tuple, List
 
 from PIL import Image
@@ -77,20 +76,8 @@ def __init__(
         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

@@ -1,4 +1,3 @@
-import warnings
 from functools import partial
 from typing import Callable, Any, Optional, List
 
@@ -18,24 +17,6 @@
 
 
 # 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

@@ -1,4 +1,3 @@
-import warnings
 from functools import partial
 from typing import Any, Callable, List, Optional, Sequence
 
@@ -22,21 +21,6 @@
 ]
 
 
-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/ops/poolers.py

@@ -1,4 +1,3 @@
-import warnings
 from typing import Optional, List, Dict, Tuple, Union
 
 import torch
@@ -287,22 +286,6 @@ def __init__(
         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],