Unverified Commit 0c2373d0 authored by Nicolas Hug's avatar Nicolas Hug Committed by GitHub
Browse files

Remove APIs that were deprecated before 0.8 (#5386)

* Remove APIs that were deprecated before 0.8

* More stuff

* Some more

* oops
parent 97eddc5d
......@@ -119,7 +119,6 @@ Transforms on PIL Image and torch.\*Tensor
RandomPerspective
RandomResizedCrop
RandomRotation
RandomSizedCrop
RandomVerticalFlip
Resize
TenCrop
......
......@@ -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():
......
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"))
......
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
......
......@@ -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
......
......@@ -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
......
......@@ -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
......
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