Remove APIs that were deprecated before 0.8 (#5386)

* Remove APIs that were deprecated before 0.8

* More stuff

* Some more

* oops

docs/source/transforms.rst

@@ -119,7 +119,6 @@ Transforms on PIL Image and torch.\*Tensor
     RandomPerspective
     RandomResizedCrop
     RandomRotation
-    RandomSizedCrop
     RandomVerticalFlip
     Resize
     TenCrop

test/test_ops.py

@@ -982,12 +982,6 @@ class TestFrozenBNT:
         bn.load_state_dict(state_dict)
         torch.testing.assert_close(fbn(x), bn(x), rtol=1e-5, atol=1e-6)
 
-    def test_frozenbatchnorm2d_n_arg(self):
-        """Ensure a warning is thrown when passing `n` kwarg
-        (remove this when support of `n` is dropped)"""
-        with pytest.warns(DeprecationWarning):
-            ops.misc.FrozenBatchNorm2d(32, eps=1e-5, n=32)
-
 
 class TestBoxConversion:
     def _get_box_sequences():

torchvision/datasets/imagenet.py

 import os
 import shutil
 import tempfile
-import warnings
 from contextlib import contextmanager
 from typing import Any, Dict, List, Iterator, Optional, Tuple
 
@@ -40,18 +39,7 @@ class ImageNet(ImageFolder):
         targets (list): The class_index value for each image in the dataset
     """
 
-    def __init__(self, root: str, split: str = "train", download: Optional[str] = None, **kwargs: Any) -> None:
-        if download is True:
-            msg = (
-                "The dataset is no longer publicly accessible. You need to "
-                "download the archives externally and place them in the root "
-                "directory."
-            )
-            raise RuntimeError(msg)
-        elif download is False:
-            msg = "The use of the download flag is deprecated, since the dataset is no longer publicly accessible."
-            warnings.warn(msg, RuntimeWarning)
-
+    def __init__(self, root: str, split: str = "train", **kwargs: Any) -> None:
         root = self.root = os.path.expanduser(root)
         self.split = verify_str_arg(split, "split", ("train", "val"))
 

torchvision/ops/misc.py

-import warnings
 from typing import Callable, List, Optional
 
 import torch
@@ -7,36 +6,6 @@ from torch import Tensor
 from ..utils import _log_api_usage_once
 
 
-class Conv2d(torch.nn.Conv2d):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        warnings.warn(
-            "torchvision.ops.misc.Conv2d is deprecated and will be "
-            "removed in future versions, use torch.nn.Conv2d instead.",
-            FutureWarning,
-        )
-
-
-class ConvTranspose2d(torch.nn.ConvTranspose2d):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        warnings.warn(
-            "torchvision.ops.misc.ConvTranspose2d is deprecated and will be "
-            "removed in future versions, use torch.nn.ConvTranspose2d instead.",
-            FutureWarning,
-        )
-
-
-class BatchNorm2d(torch.nn.BatchNorm2d):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        warnings.warn(
-            "torchvision.ops.misc.BatchNorm2d is deprecated and will be "
-            "removed in future versions, use torch.nn.BatchNorm2d instead.",
-            FutureWarning,
-        )
-
-
 interpolate = torch.nn.functional.interpolate
 
 
@@ -54,12 +23,7 @@ class FrozenBatchNorm2d(torch.nn.Module):
         self,
         num_features: int,
         eps: float = 1e-5,
-        n: Optional[int] = None,
     ):
-        # n=None for backward-compatibility
-        if n is not None:
-            warnings.warn("`n` argument is deprecated and has been renamed `num_features`", DeprecationWarning)
-            num_features = n
         super().__init__()
         _log_api_usage_once(self)
         self.eps = eps

torchvision/transforms/functional.py

@@ -438,13 +438,6 @@ def resize(
     return F_t.resize(img, size=size, interpolation=interpolation.value, max_size=max_size, antialias=antialias)
 
 
-def scale(*args, **kwargs):
-    if not torch.jit.is_scripting() and not torch.jit.is_tracing():
-        _log_api_usage_once(scale)
-    warnings.warn("The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.")
-    return resize(*args, **kwargs)
-
-
 def pad(img: Tensor, padding: List[int], fill: int = 0, padding_mode: str = "constant") -> Tensor:
     r"""Pad the given image on all sides with the given "pad" value.
     If the image is torch Tensor, it is expected

torchvision/transforms/functional_tensor.py

@@ -3,7 +3,6 @@ from typing import Optional, Tuple, List
 
 import torch
 from torch import Tensor
-from torch.jit.annotations import BroadcastingList2
 from torch.nn.functional import grid_sample, conv2d, interpolate, pad as torch_pad
 
 
@@ -249,74 +248,6 @@ def adjust_gamma(img: Tensor, gamma: float, gain: float = 1) -> Tensor:
     return result
 
 
-def center_crop(img: Tensor, output_size: BroadcastingList2[int]) -> Tensor:
-    """DEPRECATED"""
-    warnings.warn(
-        "This method is deprecated and will be removed in future releases. Please, use ``F.center_crop`` instead."
-    )
-
-    _assert_image_tensor(img)
-
-    _, image_width, image_height = img.size()
-    crop_height, crop_width = output_size
-    # crop_top = int(round((image_height - crop_height) / 2.))
-    # Result can be different between python func and scripted func
-    # Temporary workaround:
-    crop_top = int((image_height - crop_height + 1) * 0.5)
-    # crop_left = int(round((image_width - crop_width) / 2.))
-    # Result can be different between python func and scripted func
-    # Temporary workaround:
-    crop_left = int((image_width - crop_width + 1) * 0.5)
-
-    return crop(img, crop_top, crop_left, crop_height, crop_width)
-
-
-def five_crop(img: Tensor, size: BroadcastingList2[int]) -> List[Tensor]:
-    """DEPRECATED"""
-    warnings.warn(
-        "This method is deprecated and will be removed in future releases. Please, use ``F.five_crop`` instead."
-    )
-
-    _assert_image_tensor(img)
-
-    assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
-
-    _, image_width, image_height = img.size()
-    crop_height, crop_width = size
-    if crop_width > image_width or crop_height > image_height:
-        msg = "Requested crop size {} is bigger than input size {}"
-        raise ValueError(msg.format(size, (image_height, image_width)))
-
-    tl = crop(img, 0, 0, crop_width, crop_height)
-    tr = crop(img, image_width - crop_width, 0, image_width, crop_height)
-    bl = crop(img, 0, image_height - crop_height, crop_width, image_height)
-    br = crop(img, image_width - crop_width, image_height - crop_height, image_width, image_height)
-    center = center_crop(img, (crop_height, crop_width))
-
-    return [tl, tr, bl, br, center]
-
-
-def ten_crop(img: Tensor, size: BroadcastingList2[int], vertical_flip: bool = False) -> List[Tensor]:
-    """DEPRECATED"""
-    warnings.warn(
-        "This method is deprecated and will be removed in future releases. Please, use ``F.ten_crop`` instead."
-    )
-
-    _assert_image_tensor(img)
-
-    assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
-    first_five = five_crop(img, size)
-
-    if vertical_flip:
-        img = vflip(img)
-    else:
-        img = hflip(img)
-
-    second_five = five_crop(img, size)
-
-    return first_five + second_five
-
-
 def _blend(img1: Tensor, img2: Tensor, ratio: float) -> Tensor:
     ratio = float(ratio)
     bound = 1.0 if img1.is_floating_point() else 255.0

torchvision/transforms/transforms.py

@@ -26,7 +26,6 @@ __all__ = [
     "ToPILImage",
     "Normalize",
     "Resize",
-    "Scale",
     "CenterCrop",
     "Pad",
     "Lambda",
@@ -37,7 +36,6 @@ __all__ = [
     "RandomHorizontalFlip",
     "RandomVerticalFlip",
     "RandomResizedCrop",
-    "RandomSizedCrop",
     "FiveCrop",
     "TenCrop",
     "LinearTransformation",
@@ -355,17 +353,6 @@ class Resize(torch.nn.Module):
         return self.__class__.__name__ + detail
 
 
-class Scale(Resize):
-    """
-    Note: This transform is deprecated in favor of Resize.
-    """
-
-    def __init__(self, *args, **kwargs):
-        warnings.warn("The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.")
-        super().__init__(*args, **kwargs)
-        _log_api_usage_once(self)
-
-
 class CenterCrop(torch.nn.Module):
     """Crops the given image at the center.
     If the image is torch Tensor, it is expected
@@ -976,20 +963,6 @@ class RandomResizedCrop(torch.nn.Module):
         return format_string
 
 
-class RandomSizedCrop(RandomResizedCrop):
-    """
-    Note: This transform is deprecated in favor of RandomResizedCrop.
-    """
-
-    def __init__(self, *args, **kwargs):
-        warnings.warn(
-            "The use of the transforms.RandomSizedCrop transform is deprecated, "
-            + "please use transforms.RandomResizedCrop instead."
-        )
-        super().__init__(*args, **kwargs)
-        _log_api_usage_once(self)
-
-
 class FiveCrop(torch.nn.Module):
     """Crop the given image into four corners and the central crop.
     If the image is torch Tensor, it is expected