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Unverified Commit 9e71fdaf authored by Vasilis Vryniotis's avatar Vasilis Vryniotis Committed by GitHub
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Plural to singular name change. (#3055)

parent 0c445130
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Showing with 103 additions and 103 deletions
test/test_functional_tensor.py
View file @ 9e71fdaf
......@@ -9,12 +9,12 @@ import torch
import torchvision.transforms.functional_tensor as F_t
import torchvision.transforms.functional_pil as F_pil
import torchvision.transforms.functional as F
from torchvision.transforms import InterpolationModes
from torchvision.transforms import InterpolationMode
from common_utils import TransformsTester
NEAREST, BILINEAR, BICUBIC = InterpolationModes.NEAREST, InterpolationModes.BILINEAR, InterpolationModes.BICUBIC
NEAREST, BILINEAR, BICUBIC = InterpolationMode.NEAREST, InterpolationMode.BILINEAR, InterpolationMode.BICUBIC
class Tester(TransformsTester):
......@@ -419,7 +419,7 @@ class Tester(TransformsTester):
)
# assert changed type warning
with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationModes"):
with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationMode"):
res1 = F.resize(tensor, size=32, interpolation=2)
res2 = F.resize(tensor, size=32, interpolation=BILINEAR)
self.assertTrue(res1.equal(res2))
......@@ -626,7 +626,7 @@ class Tester(TransformsTester):
self.assertTrue(res1.equal(res2))
# assert changed type warning
with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationModes"):
with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationMode"):
res1 = F.affine(tensor, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=2)
res2 = F.affine(tensor, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=BILINEAR)
self.assertTrue(res1.equal(res2))
......@@ -714,7 +714,7 @@ class Tester(TransformsTester):
self.assertTrue(res1.equal(res2))
# assert changed type warning
with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationModes"):
with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationMode"):
res1 = F.rotate(tensor, 45, interpolation=2)
res2 = F.rotate(tensor, 45, interpolation=BILINEAR)
self.assertTrue(res1.equal(res2))
......@@ -788,7 +788,7 @@ class Tester(TransformsTester):
# assert changed type warning
spoints = [[0, 0], [33, 0], [33, 25], [0, 25]]
epoints = [[3, 2], [32, 3], [30, 24], [2, 25]]
with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationModes"):
with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationMode"):
res1 = F.perspective(tensor, startpoints=spoints, endpoints=epoints, interpolation=2)
res2 = F.perspective(tensor, startpoints=spoints, endpoints=epoints, interpolation=BILINEAR)
self.assertTrue(res1.equal(res2))
......
test/test_transforms.py
View file @ 9e71fdaf
......@@ -1500,12 +1500,12 @@ class Tester(unittest.TestCase):
# assert deprecation warning and non-BC
with self.assertWarnsRegex(UserWarning, r"Argument resample is deprecated and will be removed"):
t = transforms.RandomRotation((-10, 10), resample=2)
self.assertEqual(t.interpolation, transforms.InterpolationModes.BILINEAR)
self.assertEqual(t.interpolation, transforms.InterpolationMode.BILINEAR)
# assert changed type warning
with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationModes"):
with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationMode"):
t = transforms.RandomRotation((-10, 10), interpolation=2)
self.assertEqual(t.interpolation, transforms.InterpolationModes.BILINEAR)
self.assertEqual(t.interpolation, transforms.InterpolationMode.BILINEAR)
def test_random_affine(self):
......@@ -1547,22 +1547,22 @@ class Tester(unittest.TestCase):
# Checking if RandomAffine can be printed as string
t.__repr__()
t = transforms.RandomAffine(10, interpolation=transforms.InterpolationModes.BILINEAR)
t = transforms.RandomAffine(10, interpolation=transforms.InterpolationMode.BILINEAR)
self.assertIn("bilinear", t.__repr__())
# assert deprecation warning and non-BC
with self.assertWarnsRegex(UserWarning, r"Argument resample is deprecated and will be removed"):
t = transforms.RandomAffine(10, resample=2)
self.assertEqual(t.interpolation, transforms.InterpolationModes.BILINEAR)
self.assertEqual(t.interpolation, transforms.InterpolationMode.BILINEAR)
with self.assertWarnsRegex(UserWarning, r"Argument fillcolor is deprecated and will be removed"):
t = transforms.RandomAffine(10, fillcolor=10)
self.assertEqual(t.fill, 10)
# assert changed type warning
with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationModes"):
with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationMode"):
t = transforms.RandomAffine(10, interpolation=2)
self.assertEqual(t.interpolation, transforms.InterpolationModes.BILINEAR)
self.assertEqual(t.interpolation, transforms.InterpolationMode.BILINEAR)
def test_to_grayscale(self):
"""Unit tests for grayscale transform"""
......
test/test_transforms_tensor.py
View file @ 9e71fdaf
......@@ -2,7 +2,7 @@ import os
import torch
from torchvision import transforms as T
from torchvision.transforms import functional as F
from torchvision.transforms import InterpolationModes
from torchvision.transforms import InterpolationMode
import numpy as np
......@@ -11,7 +11,7 @@ import unittest
from common_utils import TransformsTester, get_tmp_dir, int_dtypes, float_dtypes
NEAREST, BILINEAR, BICUBIC = InterpolationModes.NEAREST, InterpolationModes.BILINEAR, InterpolationModes.BICUBIC
NEAREST, BILINEAR, BICUBIC = InterpolationMode.NEAREST, InterpolationMode.BILINEAR, InterpolationMode.BICUBIC
class Tester(TransformsTester):
......
torchvision/transforms/functional.py
View file @ 9e71fdaf
......@@ -20,7 +20,7 @@ from . import functional_pil as F_pil
from . import functional_tensor as F_t
class InterpolationModes(Enum):
class InterpolationMode(Enum):
"""Interpolation modes
"""
NEAREST = "nearest"
......@@ -33,26 +33,26 @@ class InterpolationModes(Enum):
# TODO: Once torchscript supports Enums with staticmethod
# this can be put into InterpolationModes as staticmethod
def _interpolation_modes_from_int(i: int) -> InterpolationModes:
# this can be put into InterpolationMode as staticmethod
def _interpolation_modes_from_int(i: int) -> InterpolationMode:
inverse_modes_mapping = {
0: InterpolationModes.NEAREST,
2: InterpolationModes.BILINEAR,
3: InterpolationModes.BICUBIC,
4: InterpolationModes.BOX,
5: InterpolationModes.HAMMING,
1: InterpolationModes.LANCZOS,
0: InterpolationMode.NEAREST,
2: InterpolationMode.BILINEAR,
3: InterpolationMode.BICUBIC,
4: InterpolationMode.BOX,
5: InterpolationMode.HAMMING,
1: InterpolationMode.LANCZOS,
}
return inverse_modes_mapping[i]
pil_modes_mapping = {
InterpolationModes.NEAREST: 0,
InterpolationModes.BILINEAR: 2,
InterpolationModes.BICUBIC: 3,
InterpolationModes.BOX: 4,
InterpolationModes.HAMMING: 5,
InterpolationModes.LANCZOS: 1,
InterpolationMode.NEAREST: 0,
InterpolationMode.BILINEAR: 2,
InterpolationMode.BICUBIC: 3,
InterpolationMode.BOX: 4,
InterpolationMode.HAMMING: 5,
InterpolationMode.LANCZOS: 1,
}
_is_pil_image = F_pil._is_pil_image
......@@ -329,7 +329,7 @@ def normalize(tensor: Tensor, mean: List[float], std: List[float], inplace: bool
return tensor
def resize(img: Tensor, size: List[int], interpolation: InterpolationModes = InterpolationModes.BILINEAR) -> Tensor:
def resize(img: Tensor, size: List[int], interpolation: InterpolationMode = InterpolationMode.BILINEAR) -> Tensor:
r"""Resize the input image to the given size.
The image can be a PIL Image or a torch Tensor, in which case it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
......@@ -343,10 +343,10 @@ def resize(img: Tensor, size: List[int], interpolation: InterpolationModes = Int
:math:`\left(\text{size} \times \frac{\text{height}}{\text{width}}, \text{size}\right)`.
In torchscript mode size as single int is not supported, use a tuple or
list of length 1: ``[size, ]``.
interpolation (InterpolationModes): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationModes`.
Default is ``InterpolationModes.BILINEAR``. If input is Tensor, only ``InterpolationModes.NEAREST``,
``InterpolationModes.BILINEAR`` and ``InterpolationModes.BICUBIC`` are supported.
interpolation (InterpolationMode): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`.
Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``,
``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
Returns:
......@@ -355,13 +355,13 @@ def resize(img: Tensor, size: List[int], interpolation: InterpolationModes = Int
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument interpolation should be of type InterpolationModes instead of int. "
"Please, use InterpolationModes enum."
"Argument interpolation should be of type InterpolationMode instead of int. "
"Please, use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
if not isinstance(interpolation, InterpolationModes):
raise TypeError("Argument interpolation should be a InterpolationModes")
if not isinstance(interpolation, InterpolationMode):
raise TypeError("Argument interpolation should be a InterpolationMode")
if not isinstance(img, torch.Tensor):
pil_interpolation = pil_modes_mapping[interpolation]
......@@ -475,7 +475,7 @@ def center_crop(img: Tensor, output_size: List[int]) -> Tensor:
def resized_crop(
img: Tensor, top: int, left: int, height: int, width: int, size: List[int],
interpolation: InterpolationModes = InterpolationModes.BILINEAR
interpolation: InterpolationMode = InterpolationMode.BILINEAR
) -> Tensor:
"""Crop the given image and resize it to desired size.
The image can be a PIL Image or a Tensor, in which case it is expected
......@@ -490,10 +490,10 @@ def resized_crop(
height (int): Height of the crop box.
width (int): Width of the crop box.
size (sequence or int): Desired output size. Same semantics as ``resize``.
interpolation (InterpolationModes): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationModes`.
Default is ``InterpolationModes.BILINEAR``. If input is Tensor, only ``InterpolationModes.NEAREST``,
``InterpolationModes.BILINEAR`` and ``InterpolationModes.BICUBIC`` are supported.
interpolation (InterpolationMode): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`.
Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``,
``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
Returns:
......@@ -556,7 +556,7 @@ def perspective(
img: Tensor,
startpoints: List[List[int]],
endpoints: List[List[int]],
interpolation: InterpolationModes = InterpolationModes.BILINEAR,
interpolation: InterpolationMode = InterpolationMode.BILINEAR,
fill: Optional[int] = None
) -> Tensor:
"""Perform perspective transform of the given image.
......@@ -569,9 +569,9 @@ def perspective(
``[top-left, top-right, bottom-right, bottom-left]`` of the original image.
endpoints (list of list of ints): List containing four lists of two integers corresponding to four corners
``[top-left, top-right, bottom-right, bottom-left]`` of the transformed image.
interpolation (InterpolationModes): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationModes`. Default is ``InterpolationModes.BILINEAR``.
If input is Tensor, only ``InterpolationModes.NEAREST``, ``InterpolationModes.BILINEAR`` are supported.
interpolation (InterpolationMode): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
fill (n-tuple or int or float): Pixel fill value for area outside the rotated
image. If int or float, the value is used for all bands respectively.
......@@ -587,13 +587,13 @@ def perspective(
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument interpolation should be of type InterpolationModes instead of int. "
"Please, use InterpolationModes enum."
"Argument interpolation should be of type InterpolationMode instead of int. "
"Please, use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
if not isinstance(interpolation, InterpolationModes):
raise TypeError("Argument interpolation should be a InterpolationModes")
if not isinstance(interpolation, InterpolationMode):
raise TypeError("Argument interpolation should be a InterpolationMode")
if not isinstance(img, torch.Tensor):
pil_interpolation = pil_modes_mapping[interpolation]
......@@ -869,7 +869,7 @@ def _get_inverse_affine_matrix(
def rotate(
img: Tensor, angle: float, interpolation: InterpolationModes = InterpolationModes.NEAREST,
img: Tensor, angle: float, interpolation: InterpolationMode = InterpolationMode.NEAREST,
expand: bool = False, center: Optional[List[int]] = None,
fill: Optional[int] = None, resample: Optional[int] = None
) -> Tensor:
......@@ -880,9 +880,9 @@ def rotate(
Args:
img (PIL Image or Tensor): image to be rotated.
angle (float or int): rotation angle value in degrees, counter-clockwise.
interpolation (InterpolationModes): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationModes`. Default is ``InterpolationModes.NEAREST``.
If input is Tensor, only ``InterpolationModes.NEAREST``, ``InterpolationModes.BILINEAR`` are supported.
interpolation (InterpolationMode): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
expand (bool, optional): Optional expansion flag.
If true, expands the output image to make it large enough to hold the entire rotated image.
......@@ -913,8 +913,8 @@ def rotate(
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument interpolation should be of type InterpolationModes instead of int. "
"Please, use InterpolationModes enum."
"Argument interpolation should be of type InterpolationMode instead of int. "
"Please, use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
......@@ -924,8 +924,8 @@ def rotate(
if center is not None and not isinstance(center, (list, tuple)):
raise TypeError("Argument center should be a sequence")
if not isinstance(interpolation, InterpolationModes):
raise TypeError("Argument interpolation should be a InterpolationModes")
if not isinstance(interpolation, InterpolationMode):
raise TypeError("Argument interpolation should be a InterpolationMode")
if not isinstance(img, torch.Tensor):
pil_interpolation = pil_modes_mapping[interpolation]
......@@ -945,7 +945,7 @@ def rotate(
def affine(
img: Tensor, angle: float, translate: List[int], scale: float, shear: List[float],
interpolation: InterpolationModes = InterpolationModes.NEAREST, fill: Optional[int] = None,
interpolation: InterpolationMode = InterpolationMode.NEAREST, fill: Optional[int] = None,
resample: Optional[int] = None, fillcolor: Optional[int] = None
) -> Tensor:
"""Apply affine transformation on the image keeping image center invariant.
......@@ -960,9 +960,9 @@ def affine(
shear (float or tuple or list): shear angle value in degrees between -180 to 180, clockwise direction.
If a tuple of list is specified, the first value corresponds to a shear parallel to the x axis, while
the second value corresponds to a shear parallel to the y axis.
interpolation (InterpolationModes): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationModes`. Default is ``InterpolationModes.NEAREST``.
If input is Tensor, only ``InterpolationModes.NEAREST``, ``InterpolationModes.BILINEAR`` are supported.
interpolation (InterpolationMode): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
fill (int): Optional fill color for the area outside the transform in the output image (Pillow>=5.0.0).
This option is not supported for Tensor input. Fill value for the area outside the transform in the output
......@@ -984,8 +984,8 @@ def affine(
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument interpolation should be of type InterpolationModes instead of int. "
"Please, use InterpolationModes enum."
"Argument interpolation should be of type InterpolationMode instead of int. "
"Please, use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
......@@ -1010,8 +1010,8 @@ def affine(
if not isinstance(shear, (numbers.Number, (list, tuple))):
raise TypeError("Shear should be either a single value or a sequence of two values")
if not isinstance(interpolation, InterpolationModes):
raise TypeError("Argument interpolation should be a InterpolationModes")
if not isinstance(interpolation, InterpolationMode):
raise TypeError("Argument interpolation should be a InterpolationMode")
if isinstance(angle, int):
angle = float(angle)
......
torchvision/transforms/transforms.py
View file @ 9e71fdaf
......@@ -14,14 +14,14 @@ except ImportError:
accimage = None
from . import functional as F
from .functional import InterpolationModes, _interpolation_modes_from_int
from .functional import InterpolationMode, _interpolation_modes_from_int
__all__ = ["Compose", "ToTensor", "PILToTensor", "ConvertImageDtype", "ToPILImage", "Normalize", "Resize", "Scale",
"CenterCrop", "Pad", "Lambda", "RandomApply", "RandomChoice", "RandomOrder", "RandomCrop",
"RandomHorizontalFlip", "RandomVerticalFlip", "RandomResizedCrop", "RandomSizedCrop", "FiveCrop", "TenCrop",
"LinearTransformation", "ColorJitter", "RandomRotation", "RandomAffine", "Grayscale", "RandomGrayscale",
"RandomPerspective", "RandomErasing", "GaussianBlur", "InterpolationModes"]
"RandomPerspective", "RandomErasing", "GaussianBlur", "InterpolationMode"]
class Compose:
......@@ -234,15 +234,15 @@ class Resize(torch.nn.Module):
(size * height / width, size).
In torchscript mode padding as single int is not supported, use a tuple or
list of length 1: ``[size, ]``.
interpolation (InterpolationModes): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationModes`. Default is ``InterpolationModes.BILINEAR``.
If input is Tensor, only ``InterpolationModes.NEAREST``, ``InterpolationModes.BILINEAR`` and
``InterpolationModes.BICUBIC`` are supported.
interpolation (InterpolationMode): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` and
``InterpolationMode.BICUBIC`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
"""
def __init__(self, size, interpolation=InterpolationModes.BILINEAR):
def __init__(self, size, interpolation=InterpolationMode.BILINEAR):
super().__init__()
if not isinstance(size, (int, Sequence)):
raise TypeError("Size should be int or sequence. Got {}".format(type(size)))
......@@ -253,8 +253,8 @@ class Resize(torch.nn.Module):
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument interpolation should be of type InterpolationModes instead of int. "
"Please, use InterpolationModes enum."
"Argument interpolation should be of type InterpolationMode instead of int. "
"Please, use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
......@@ -663,9 +663,9 @@ class RandomPerspective(torch.nn.Module):
distortion_scale (float): argument to control the degree of distortion and ranges from 0 to 1.
Default is 0.5.
p (float): probability of the image being transformed. Default is 0.5.
interpolation (InterpolationModes): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationModes`. Default is ``InterpolationModes.BILINEAR``.
If input is Tensor, only ``InterpolationModes.NEAREST``, ``InterpolationModes.BILINEAR`` are supported.
interpolation (InterpolationMode): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
fill (n-tuple or int or float): Pixel fill value for area outside the rotated
image. If int or float, the value is used for all bands respectively. Default is 0.
......@@ -673,15 +673,15 @@ class RandomPerspective(torch.nn.Module):
input. Fill value for the area outside the transform in the output image is always 0.
"""
def __init__(self, distortion_scale=0.5, p=0.5, interpolation=InterpolationModes.BILINEAR, fill=0):
def __init__(self, distortion_scale=0.5, p=0.5, interpolation=InterpolationMode.BILINEAR, fill=0):
super().__init__()
self.p = p
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument interpolation should be of type InterpolationModes instead of int. "
"Please, use InterpolationModes enum."
"Argument interpolation should be of type InterpolationMode instead of int. "
"Please, use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
......@@ -758,15 +758,15 @@ class RandomResizedCrop(torch.nn.Module):
made. If provided a tuple or list of length 1, it will be interpreted as (size[0], size[0]).
scale (tuple of float): scale range of the cropped image before resizing, relatively to the origin image.
ratio (tuple of float): aspect ratio range of the cropped image before resizing.
interpolation (InterpolationModes): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationModes`. Default is ``InterpolationModes.BILINEAR``.
If input is Tensor, only ``InterpolationModes.NEAREST``, ``InterpolationModes.BILINEAR`` and
``InterpolationModes.BICUBIC`` are supported.
interpolation (InterpolationMode): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` and
``InterpolationMode.BICUBIC`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
"""
def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.), interpolation=InterpolationModes.BILINEAR):
def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.), interpolation=InterpolationMode.BILINEAR):
super().__init__()
self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
......@@ -780,8 +780,8 @@ class RandomResizedCrop(torch.nn.Module):
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument interpolation should be of type InterpolationModes instead of int. "
"Please, use InterpolationModes enum."
"Argument interpolation should be of type InterpolationMode instead of int. "
"Please, use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
......@@ -1147,9 +1147,9 @@ class RandomRotation(torch.nn.Module):
degrees (sequence or float or int): Range of degrees to select from.
If degrees is a number instead of sequence like (min, max), the range of degrees
will be (-degrees, +degrees).
interpolation (InterpolationModes): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationModes`. Default is ``InterpolationModes.NEAREST``.
If input is Tensor, only ``InterpolationModes.NEAREST``, ``InterpolationModes.BILINEAR`` are supported.
interpolation (InterpolationMode): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
expand (bool, optional): Optional expansion flag.
If true, expands the output to make it large enough to hold the entire rotated image.
......@@ -1170,7 +1170,7 @@ class RandomRotation(torch.nn.Module):
"""
def __init__(
self, degrees, interpolation=InterpolationModes.NEAREST, expand=False, center=None, fill=None, resample=None
self, degrees, interpolation=InterpolationMode.NEAREST, expand=False, center=None, fill=None, resample=None
):
super().__init__()
if resample is not None:
......@@ -1182,8 +1182,8 @@ class RandomRotation(torch.nn.Module):
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument interpolation should be of type InterpolationModes instead of int. "
"Please, use InterpolationModes enum."
"Argument interpolation should be of type InterpolationMode instead of int. "
"Please, use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
......@@ -1253,9 +1253,9 @@ class RandomAffine(torch.nn.Module):
range (shear[0], shear[1]) will be applied. Else if shear is a tuple or list of 4 values,
a x-axis shear in (shear[0], shear[1]) and y-axis shear in (shear[2], shear[3]) will be applied.
Will not apply shear by default.
interpolation (InterpolationModes): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationModes`. Default is ``InterpolationModes.NEAREST``.
If input is Tensor, only ``InterpolationModes.NEAREST``, ``InterpolationModes.BILINEAR`` are supported.
interpolation (InterpolationMode): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
fill (tuple or int): Optional fill color (Tuple for RGB Image and int for grayscale) for the area
outside the transform in the output image (Pillow>=5.0.0). This option is not supported for Tensor
......@@ -1270,7 +1270,7 @@ class RandomAffine(torch.nn.Module):
"""
def __init__(
self, degrees, translate=None, scale=None, shear=None, interpolation=InterpolationModes.NEAREST, fill=0,
self, degrees, translate=None, scale=None, shear=None, interpolation=InterpolationMode.NEAREST, fill=0,
fillcolor=None, resample=None
):
super().__init__()
......@@ -1283,8 +1283,8 @@ class RandomAffine(torch.nn.Module):
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument interpolation should be of type InterpolationModes instead of int. "
"Please, use InterpolationModes enum."
"Argument interpolation should be of type InterpolationMode instead of int. "
"Please, use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
......@@ -1377,7 +1377,7 @@ class RandomAffine(torch.nn.Module):
s += ', scale={scale}'
if self.shear is not None:
s += ', shear={shear}'
if self.interpolation != InterpolationModes.NEAREST:
if self.interpolation != InterpolationMode.NEAREST:
s += ', interpolation={interpolation}'
if self.fill != 0:
s += ', fill={fill}'
......
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