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Unverified Commit 0c445130 authored by vfdev's avatar vfdev Committed by GitHub
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[BC-breaking] Introduced InterpolationModes and deprecated arguments: resample... 

[BC-breaking] Introduced InterpolationModes and deprecated arguments: resample and fillcolor (#2952)

* Deprecated arguments: resample and fillcolor
Replaced by interpolation and fill

* Updates according to the review

* Added tests to check warnings and asserted BC

* [WIP] Interpolation modes

* Added InterpolationModes enum

* Added supported for int values for interpolation for BC

* Removed useless test code

* Fix flake8
parent 240210c9
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Showing with 416 additions and 177 deletions
test/test_functional_tensor.py
View file @ 0c445130
......@@ -4,16 +4,19 @@ import colorsys
import math
import numpy as np
from PIL.Image import NEAREST, BILINEAR, BICUBIC
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 common_utils import TransformsTester
NEAREST, BILINEAR, BICUBIC = InterpolationModes.NEAREST, InterpolationModes.BILINEAR, InterpolationModes.BICUBIC
class Tester(TransformsTester):
def setUp(self):
......@@ -365,7 +368,7 @@ class Tester(TransformsTester):
)
def test_resize(self):
script_fn = torch.jit.script(F_t.resize)
script_fn = torch.jit.script(F.resize)
tensor, pil_img = self._create_data(26, 36, device=self.device)
batch_tensors = self._create_data_batch(16, 18, num_samples=4, device=self.device)
......@@ -382,14 +385,14 @@ class Tester(TransformsTester):
for size in [32, 26, [32, ], [32, 32], (32, 32), [26, 35]]:
for interpolation in [BILINEAR, BICUBIC, NEAREST]:
resized_tensor = F_t.resize(tensor, size=size, interpolation=interpolation)
resized_pil_img = F_pil.resize(pil_img, size=size, interpolation=interpolation)
resized_tensor = F.resize(tensor, size=size, interpolation=interpolation)
resized_pil_img = F.resize(pil_img, size=size, interpolation=interpolation)
self.assertEqual(
resized_tensor.size()[1:], resized_pil_img.size[::-1], msg="{}, {}".format(size, interpolation)
)
if interpolation != NEAREST:
if interpolation not in [NEAREST, ]:
# We can not check values if mode = NEAREST, as results are different
# E.g. resized_tensor = [[a, a, b, c, d, d, e, ...]]
# E.g. resized_pil_img = [[a, b, c, c, d, e, f, ...]]
......@@ -407,6 +410,7 @@ class Tester(TransformsTester):
script_size = [size, ]
else:
script_size = size
resize_result = script_fn(tensor, size=script_size, interpolation=interpolation)
self.assertTrue(resized_tensor.equal(resize_result), msg="{}, {}".format(size, interpolation))
......@@ -414,17 +418,24 @@ class Tester(TransformsTester):
batch_tensors, F.resize, size=script_size, interpolation=interpolation
)
# assert changed type warning
with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationModes"):
res1 = F.resize(tensor, size=32, interpolation=2)
res2 = F.resize(tensor, size=32, interpolation=BILINEAR)
self.assertTrue(res1.equal(res2))
def test_resized_crop(self):
# test values of F.resized_crop in several cases:
# 1) resize to the same size, crop to the same size => should be identity
tensor, _ = self._create_data(26, 36, device=self.device)
for i in [0, 2, 3]:
out_tensor = F.resized_crop(tensor, top=0, left=0, height=26, width=36, size=[26, 36], interpolation=i)
for mode in [NEAREST, BILINEAR, BICUBIC]:
out_tensor = F.resized_crop(tensor, top=0, left=0, height=26, width=36, size=[26, 36], interpolation=mode)
self.assertTrue(tensor.equal(out_tensor), msg="{} vs {}".format(out_tensor[0, :5, :5], tensor[0, :5, :5]))
# 2) resize by half and crop a TL corner
tensor, _ = self._create_data(26, 36, device=self.device)
out_tensor = F.resized_crop(tensor, top=0, left=0, height=20, width=30, size=[10, 15], interpolation=0)
out_tensor = F.resized_crop(tensor, top=0, left=0, height=20, width=30, size=[10, 15], interpolation=NEAREST)
expected_out_tensor = tensor[:, :20:2, :30:2]
self.assertTrue(
expected_out_tensor.equal(out_tensor),
......@@ -433,17 +444,19 @@ class Tester(TransformsTester):
batch_tensors = self._create_data_batch(26, 36, num_samples=4, device=self.device)
self._test_fn_on_batch(
batch_tensors, F.resized_crop, top=1, left=2, height=20, width=30, size=[10, 15], interpolation=0
batch_tensors, F.resized_crop, top=1, left=2, height=20, width=30, size=[10, 15], interpolation=NEAREST
)
def _test_affine_identity_map(self, tensor, scripted_affine):
# 1) identity map
out_tensor = F.affine(tensor, angle=0, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=0)
out_tensor = F.affine(tensor, angle=0, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST)
self.assertTrue(
tensor.equal(out_tensor), msg="{} vs {}".format(out_tensor[0, :5, :5], tensor[0, :5, :5])
)
out_tensor = scripted_affine(tensor, angle=0, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=0)
out_tensor = scripted_affine(
tensor, angle=0, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST
)
self.assertTrue(
tensor.equal(out_tensor), msg="{} vs {}".format(out_tensor[0, :5, :5], tensor[0, :5, :5])
)
......@@ -461,13 +474,13 @@ class Tester(TransformsTester):
]
for a, true_tensor in test_configs:
out_pil_img = F.affine(
pil_img, angle=a, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=0
pil_img, angle=a, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST
)
out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1))).to(self.device)
for fn in [F.affine, scripted_affine]:
out_tensor = fn(
tensor, angle=a, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=0
tensor, angle=a, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST
)
if true_tensor is not None:
self.assertTrue(
......@@ -496,13 +509,13 @@ class Tester(TransformsTester):
for a in test_configs:
out_pil_img = F.affine(
pil_img, angle=a, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=0
pil_img, angle=a, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST
)
out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))
for fn in [F.affine, scripted_affine]:
out_tensor = fn(
tensor, angle=a, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=0
tensor, angle=a, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST
).cpu()
if out_tensor.dtype != torch.uint8:
......@@ -526,10 +539,10 @@ class Tester(TransformsTester):
]
for t in test_configs:
out_pil_img = F.affine(pil_img, angle=0, translate=t, scale=1.0, shear=[0.0, 0.0], resample=0)
out_pil_img = F.affine(pil_img, angle=0, translate=t, scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST)
for fn in [F.affine, scripted_affine]:
out_tensor = fn(tensor, angle=0, translate=t, scale=1.0, shear=[0.0, 0.0], resample=0)
out_tensor = fn(tensor, angle=0, translate=t, scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST)
if out_tensor.dtype != torch.uint8:
out_tensor = out_tensor.to(torch.uint8)
......@@ -550,13 +563,13 @@ class Tester(TransformsTester):
(-45, [-10, -10], 1.2, [4.0, 5.0]),
(-90, [0, 0], 1.0, [0.0, 0.0]),
]
for r in [0, ]:
for r in [NEAREST, ]:
for a, t, s, sh in test_configs:
out_pil_img = F.affine(pil_img, angle=a, translate=t, scale=s, shear=sh, resample=r)
out_pil_img = F.affine(pil_img, angle=a, translate=t, scale=s, shear=sh, interpolation=r)
out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))
for fn in [F.affine, scripted_affine]:
out_tensor = fn(tensor, angle=a, translate=t, scale=s, shear=sh, resample=r).cpu()
out_tensor = fn(tensor, angle=a, translate=t, scale=s, shear=sh, interpolation=r).cpu()
if out_tensor.dtype != torch.uint8:
out_tensor = out_tensor.to(torch.uint8)
......@@ -605,18 +618,36 @@ class Tester(TransformsTester):
batch_tensors, F.affine, angle=-43, translate=[-3, 4], scale=1.2, shear=[4.0, 5.0]
)
tensor, pil_img = data[0]
# assert deprecation warning and non-BC
with self.assertWarnsRegex(UserWarning, r"Argument resample is deprecated and will be removed"):
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)
self.assertTrue(res1.equal(res2))
# assert changed type warning
with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationModes"):
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))
with self.assertWarnsRegex(UserWarning, r"Argument fillcolor is deprecated and will be removed"):
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)
self.assertEqual(res1, res2)
def _test_rotate_all_options(self, tensor, pil_img, scripted_rotate, centers):
img_size = pil_img.size
dt = tensor.dtype
for r in [0, ]:
for r in [NEAREST, ]:
for a in range(-180, 180, 17):
for e in [True, False]:
for c in centers:
out_pil_img = F.rotate(pil_img, angle=a, resample=r, expand=e, center=c)
out_pil_img = F.rotate(pil_img, angle=a, interpolation=r, expand=e, center=c)
out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))
for fn in [F.rotate, scripted_rotate]:
out_tensor = fn(tensor, angle=a, resample=r, expand=e, center=c).cpu()
out_tensor = fn(tensor, angle=a, interpolation=r, expand=e, center=c).cpu()
if out_tensor.dtype != torch.uint8:
out_tensor = out_tensor.to(torch.uint8)
......@@ -673,12 +704,24 @@ class Tester(TransformsTester):
center = (20, 22)
self._test_fn_on_batch(
batch_tensors, F.rotate, angle=32, resample=0, expand=True, center=center
batch_tensors, F.rotate, angle=32, interpolation=NEAREST, expand=True, center=center
)
tensor, pil_img = data[0]
# assert deprecation warning and non-BC
with self.assertWarnsRegex(UserWarning, r"Argument resample is deprecated and will be removed"):
res1 = F.rotate(tensor, 45, resample=2)
res2 = F.rotate(tensor, 45, interpolation=BILINEAR)
self.assertTrue(res1.equal(res2))
# assert changed type warning
with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationModes"):
res1 = F.rotate(tensor, 45, interpolation=2)
res2 = F.rotate(tensor, 45, interpolation=BILINEAR)
self.assertTrue(res1.equal(res2))
def _test_perspective(self, tensor, pil_img, scripted_transform, test_configs):
dt = tensor.dtype
for r in [0, ]:
for r in [NEAREST, ]:
for spoints, epoints in test_configs:
out_pil_img = F.perspective(pil_img, startpoints=spoints, endpoints=epoints, interpolation=r)
out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))
......@@ -739,9 +782,17 @@ class Tester(TransformsTester):
for spoints, epoints in test_configs:
self._test_fn_on_batch(
batch_tensors, F.perspective, startpoints=spoints, endpoints=epoints, interpolation=0
batch_tensors, F.perspective, startpoints=spoints, endpoints=epoints, interpolation=NEAREST
)
# 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"):
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))
def test_gaussian_blur(self):
small_image_tensor = torch.from_numpy(
np.arange(3 * 10 * 12, dtype="uint8").reshape((10, 12, 3))
......
test/test_transforms.py
View file @ 0c445130
......@@ -1492,11 +1492,21 @@ class Tester(unittest.TestCase):
t = transforms.RandomRotation((-10, 10))
angle = t.get_params(t.degrees)
self.assertTrue(angle > -10 and angle < 10)
self.assertTrue(-10 < angle < 10)
# Checking if RandomRotation can be printed as string
t.__repr__()
# 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)
# assert changed type warning
with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationModes"):
t = transforms.RandomRotation((-10, 10), interpolation=2)
self.assertEqual(t.interpolation, transforms.InterpolationModes.BILINEAR)
def test_random_affine(self):
with self.assertRaises(ValueError):
......@@ -1537,8 +1547,22 @@ class Tester(unittest.TestCase):
# Checking if RandomAffine can be printed as string
t.__repr__()
t = transforms.RandomAffine(10, resample=Image.BILINEAR)
self.assertIn("Image.BILINEAR", t.__repr__())
t = transforms.RandomAffine(10, interpolation=transforms.InterpolationModes.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)
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"):
t = transforms.RandomAffine(10, interpolation=2)
self.assertEqual(t.interpolation, transforms.InterpolationModes.BILINEAR)
def test_to_grayscale(self):
"""Unit tests for grayscale transform"""
......
test/test_transforms_tensor.py
View file @ 0c445130
......@@ -2,8 +2,7 @@ import os
import torch
from torchvision import transforms as T
from torchvision.transforms import functional as F
from PIL.Image import NEAREST, BILINEAR, BICUBIC
from torchvision.transforms import InterpolationModes
import numpy as np
......@@ -12,6 +11,9 @@ import unittest
from common_utils import TransformsTester, get_tmp_dir, int_dtypes, float_dtypes
NEAREST, BILINEAR, BICUBIC = InterpolationModes.NEAREST, InterpolationModes.BILINEAR, InterpolationModes.BICUBIC
class Tester(TransformsTester):
def setUp(self):
......@@ -349,7 +351,7 @@ class Tester(TransformsTester):
for interpolation in [NEAREST, BILINEAR]:
transform = T.RandomAffine(
degrees=degrees, translate=translate,
scale=scale, shear=shear, resample=interpolation
scale=scale, shear=shear, interpolation=interpolation
)
s_transform = torch.jit.script(transform)
......@@ -368,7 +370,7 @@ class Tester(TransformsTester):
for degrees in [45, 35.0, (-45, 45), [-90.0, 90.0]]:
for interpolation in [NEAREST, BILINEAR]:
transform = T.RandomRotation(
degrees=degrees, resample=interpolation, expand=expand, center=center
degrees=degrees, interpolation=interpolation, expand=expand, center=center
)
s_transform = torch.jit.script(transform)
......
torchvision/transforms/functional.py
View file @ 0c445130
import math
import numbers
import warnings
from enum import Enum
from typing import Any, Optional
import numpy as np
......@@ -19,6 +20,41 @@ from . import functional_pil as F_pil
from . import functional_tensor as F_t
class InterpolationModes(Enum):
"""Interpolation modes
"""
NEAREST = "nearest"
BILINEAR = "bilinear"
BICUBIC = "bicubic"
# For PIL compatibility
BOX = "box"
HAMMING = "hamming"
LANCZOS = "lanczos"
# TODO: Once torchscript supports Enums with staticmethod
# this can be put into InterpolationModes as staticmethod
def _interpolation_modes_from_int(i: int) -> InterpolationModes:
inverse_modes_mapping = {
0: InterpolationModes.NEAREST,
2: InterpolationModes.BILINEAR,
3: InterpolationModes.BICUBIC,
4: InterpolationModes.BOX,
5: InterpolationModes.HAMMING,
1: InterpolationModes.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,
}
_is_pil_image = F_pil._is_pil_image
_parse_fill = F_pil._parse_fill
......@@ -293,7 +329,7 @@ def normalize(tensor: Tensor, mean: List[float], std: List[float], inplace: bool
return tensor
def resize(img: Tensor, size: List[int], interpolation: int = Image.BILINEAR) -> Tensor:
def resize(img: Tensor, size: List[int], interpolation: InterpolationModes = InterpolationModes.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
......@@ -307,17 +343,31 @@ def resize(img: Tensor, size: List[int], interpolation: int = Image.BILINEAR) ->
: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 (int, optional): Desired interpolation enum defined by `filters`_.
Default is ``PIL.Image.BILINEAR``. If input is Tensor, only ``PIL.Image.NEAREST``, ``PIL.Image.BILINEAR``
and ``PIL.Image.BICUBIC`` are supported.
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.
For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
Returns:
PIL Image or Tensor: Resized image.
"""
# 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."
)
interpolation = _interpolation_modes_from_int(interpolation)
if not isinstance(interpolation, InterpolationModes):
raise TypeError("Argument interpolation should be a InterpolationModes")
if not isinstance(img, torch.Tensor):
return F_pil.resize(img, size=size, interpolation=interpolation)
pil_interpolation = pil_modes_mapping[interpolation]
return F_pil.resize(img, size=size, interpolation=pil_interpolation)
return F_t.resize(img, size=size, interpolation=interpolation)
return F_t.resize(img, size=size, interpolation=interpolation.value)
def scale(*args, **kwargs):
......@@ -424,7 +474,8 @@ 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: int = Image.BILINEAR
img: Tensor, top: int, left: int, height: int, width: int, size: List[int],
interpolation: InterpolationModes = InterpolationModes.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
......@@ -439,9 +490,12 @@ 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 (int, optional): Desired interpolation enum defined by `filters`_.
Default is ``PIL.Image.BILINEAR``. If input is Tensor, only ``PIL.Image.NEAREST``, ``PIL.Image.BILINEAR``
and ``PIL.Image.BICUBIC`` are supported.
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.
For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
Returns:
PIL Image or Tensor: Cropped image.
"""
......@@ -502,7 +556,7 @@ def perspective(
img: Tensor,
startpoints: List[List[int]],
endpoints: List[List[int]],
interpolation: int = 2,
interpolation: InterpolationModes = InterpolationModes.BILINEAR,
fill: Optional[int] = None
) -> Tensor:
"""Perform perspective transform of the given image.
......@@ -515,8 +569,10 @@ 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 (int): Interpolation type. If input is Tensor, only ``PIL.Image.NEAREST`` and
``PIL.Image.BILINEAR`` are supported. Default, ``PIL.Image.BILINEAR`` for PIL images and Tensors.
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.
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.
This option is only available for ``pillow>=5.0.0``. This option is not supported for Tensor
......@@ -528,10 +584,22 @@ def perspective(
coeffs = _get_perspective_coeffs(startpoints, endpoints)
# 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."
)
interpolation = _interpolation_modes_from_int(interpolation)
if not isinstance(interpolation, InterpolationModes):
raise TypeError("Argument interpolation should be a InterpolationModes")
if not isinstance(img, torch.Tensor):
return F_pil.perspective(img, coeffs, interpolation=interpolation, fill=fill)
pil_interpolation = pil_modes_mapping[interpolation]
return F_pil.perspective(img, coeffs, interpolation=pil_interpolation, fill=fill)
return F_t.perspective(img, coeffs, interpolation=interpolation, fill=fill)
return F_t.perspective(img, coeffs, interpolation=interpolation.value, fill=fill)
def vflip(img: Tensor) -> Tensor:
......@@ -801,8 +869,9 @@ def _get_inverse_affine_matrix(
def rotate(
img: Tensor, angle: float, resample: int = 0, expand: bool = False,
center: Optional[List[int]] = None, fill: Optional[int] = None
img: Tensor, angle: float, interpolation: InterpolationModes = InterpolationModes.NEAREST,
expand: bool = False, center: Optional[List[int]] = None,
fill: Optional[int] = None, resample: Optional[int] = None
) -> Tensor:
"""Rotate the image by angle.
The image can be a PIL Image or a Tensor, in which case it is expected
......@@ -811,9 +880,10 @@ def rotate(
Args:
img (PIL Image or Tensor): image to be rotated.
angle (float or int): rotation angle value in degrees, counter-clockwise.
resample (``PIL.Image.NEAREST`` or ``PIL.Image.BILINEAR`` or ``PIL.Image.BICUBIC``, optional):
An optional resampling filter. See `filters`_ for more information.
If omitted, or if the image has mode "1" or "P", it is set to ``PIL.Image.NEAREST``.
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.
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.
If false or omitted, make the output image the same size as the input image.
......@@ -825,6 +895,8 @@ def rotate(
Defaults to 0 for all bands. This option is only available for ``pillow>=5.2.0``.
This option is not supported for Tensor input. Fill value for the area outside the transform in the output
image is always 0.
resample (int, optional): deprecated argument and will be removed since v0.10.0.
Please use `arg`:interpolation: instead.
Returns:
PIL Image or Tensor: Rotated image.
......@@ -832,14 +904,32 @@ def rotate(
.. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters
"""
if resample is not None:
warnings.warn(
"Argument resample is deprecated and will be removed since v0.10.0. Please, use interpolation instead"
)
interpolation = _interpolation_modes_from_int(resample)
# 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."
)
interpolation = _interpolation_modes_from_int(interpolation)
if not isinstance(angle, (int, float)):
raise TypeError("Argument angle should be int or float")
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(img, torch.Tensor):
return F_pil.rotate(img, angle=angle, resample=resample, expand=expand, center=center, fill=fill)
pil_interpolation = pil_modes_mapping[interpolation]
return F_pil.rotate(img, angle=angle, interpolation=pil_interpolation, expand=expand, center=center, fill=fill)
center_f = [0.0, 0.0]
if center is not None:
......@@ -850,12 +940,13 @@ def rotate(
# due to current incoherence of rotation angle direction between affine and rotate implementations
# we need to set -angle.
matrix = _get_inverse_affine_matrix(center_f, -angle, [0.0, 0.0], 1.0, [0.0, 0.0])
return F_t.rotate(img, matrix=matrix, resample=resample, expand=expand, fill=fill)
return F_t.rotate(img, matrix=matrix, interpolation=interpolation.value, expand=expand, fill=fill)
def affine(
img: Tensor, angle: float, translate: List[int], scale: float, shear: List[float],
resample: int = 0, fillcolor: Optional[int] = None
interpolation: InterpolationModes = InterpolationModes.NEAREST, fill: Optional[int] = None,
resample: Optional[int] = None, fillcolor: Optional[int] = None
) -> Tensor:
"""Apply affine transformation on the image keeping image center invariant.
The image can be a PIL Image or a Tensor, in which case it is expected
......@@ -869,17 +960,41 @@ 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.
resample (``PIL.Image.NEAREST`` or ``PIL.Image.BILINEAR`` or ``PIL.Image.BICUBIC``, optional):
An optional resampling filter. See `filters`_ for more information.
If omitted, or if the image is PIL Image and has mode "1" or "P", it is set to ``PIL.Image.NEAREST``.
If input is Tensor, only ``PIL.Image.NEAREST`` and ``PIL.Image.BILINEAR`` are supported.
fillcolor (int): Optional fill color for the area outside the transform in the output image (Pillow>=5.0.0).
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.
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
image is always 0.
fillcolor (tuple or int, optional): deprecated argument and will be removed since v0.10.0.
Please use `arg`:fill: instead.
resample (int, optional): deprecated argument and will be removed since v0.10.0.
Please use `arg`:interpolation: instead.
Returns:
PIL Image or Tensor: Transformed image.
"""
if resample is not None:
warnings.warn(
"Argument resample is deprecated and will be removed since v0.10.0. Please, use interpolation instead"
)
interpolation = _interpolation_modes_from_int(resample)
# 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."
)
interpolation = _interpolation_modes_from_int(interpolation)
if fillcolor is not None:
warnings.warn(
"Argument fillcolor is deprecated and will be removed since v0.10.0. Please, use fill instead"
)
fill = fillcolor
if not isinstance(angle, (int, float)):
raise TypeError("Argument angle should be int or float")
......@@ -895,6 +1010,9 @@ 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 isinstance(angle, int):
angle = float(angle)
......@@ -920,12 +1038,12 @@ def affine(
# otherwise image rotated by 90 degrees is shifted vs output image of torch.rot90 or F_t.affine
center = [img_size[0] * 0.5, img_size[1] * 0.5]
matrix = _get_inverse_affine_matrix(center, angle, translate, scale, shear)
return F_pil.affine(img, matrix=matrix, resample=resample, fillcolor=fillcolor)
pil_interpolation = pil_modes_mapping[interpolation]
return F_pil.affine(img, matrix=matrix, interpolation=pil_interpolation, fill=fill)
translate_f = [1.0 * t for t in translate]
matrix = _get_inverse_affine_matrix([0.0, 0.0], angle, translate_f, scale, shear)
return F_t.affine(img, matrix=matrix, resample=resample, fillcolor=fillcolor)
return F_t.affine(img, matrix=matrix, interpolation=interpolation.value, fill=fill)
@torch.jit.unused
......
torchvision/transforms/functional_pil.py
View file @ 0c445130
......@@ -474,7 +474,7 @@ def _parse_fill(fill, img, min_pil_version, name="fillcolor"):
@torch.jit.unused
def affine(img, matrix, resample=0, fillcolor=None):
def affine(img, matrix, interpolation=0, fill=None):
"""PRIVATE METHOD. Apply affine transformation on the PIL Image keeping image center invariant.
.. warning::
......@@ -485,11 +485,11 @@ def affine(img, matrix, resample=0, fillcolor=None):
Args:
img (PIL Image): image to be rotated.
matrix (list of floats): list of 6 float values representing inverse matrix for affine transformation.
resample (``PIL.Image.NEAREST`` or ``PIL.Image.BILINEAR`` or ``PIL.Image.BICUBIC``, optional):
interpolation (``PIL.Image.NEAREST`` or ``PIL.Image.BILINEAR`` or ``PIL.Image.BICUBIC``, optional):
An optional resampling filter.
See `filters`_ for more information.
If omitted, or if the image has mode "1" or "P", it is set to ``PIL.Image.NEAREST``.
fillcolor (int): Optional fill color for the area outside the transform in the output image. (Pillow>=5.0.0)
fill (int): Optional fill color for the area outside the transform in the output image. (Pillow>=5.0.0)
Returns:
PIL Image: Transformed image.
......@@ -498,12 +498,12 @@ def affine(img, matrix, resample=0, fillcolor=None):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
output_size = img.size
opts = _parse_fill(fillcolor, img, '5.0.0')
return img.transform(output_size, Image.AFFINE, matrix, resample, **opts)
opts = _parse_fill(fill, img, '5.0.0')
return img.transform(output_size, Image.AFFINE, matrix, interpolation, **opts)
@torch.jit.unused
def rotate(img, angle, resample=0, expand=False, center=None, fill=None):
def rotate(img, angle, interpolation=0, expand=False, center=None, fill=None):
"""PRIVATE METHOD. Rotate PIL image by angle.
.. warning::
......@@ -514,7 +514,7 @@ def rotate(img, angle, resample=0, expand=False, center=None, fill=None):
Args:
img (PIL Image): image to be rotated.
angle (float or int): rotation angle value in degrees, counter-clockwise.
resample (``PIL.Image.NEAREST`` or ``PIL.Image.BILINEAR`` or ``PIL.Image.BICUBIC``, optional):
interpolation (``PIL.Image.NEAREST`` or ``PIL.Image.BILINEAR`` or ``PIL.Image.BICUBIC``, optional):
An optional resampling filter. See `filters`_ for more information.
If omitted, or if the image has mode "1" or "P", it is set to ``PIL.Image.NEAREST``.
expand (bool, optional): Optional expansion flag.
......@@ -538,7 +538,7 @@ def rotate(img, angle, resample=0, expand=False, center=None, fill=None):
raise TypeError("img should be PIL Image. Got {}".format(type(img)))
opts = _parse_fill(fill, img, '5.2.0')
return img.rotate(angle, resample, expand, center, **opts)
return img.rotate(angle, interpolation, expand, center, **opts)
@torch.jit.unused
......
torchvision/transforms/functional_tensor.py
View file @ 0c445130
......@@ -757,7 +757,7 @@ def pad(img: Tensor, padding: List[int], fill: int = 0, padding_mode: str = "con
return img
def resize(img: Tensor, size: List[int], interpolation: int = 2) -> Tensor:
def resize(img: Tensor, size: List[int], interpolation: str = "bilinear") -> Tensor:
r"""PRIVATE METHOD. Resize the input Tensor to the given size.
.. warning::
......@@ -774,8 +774,8 @@ def resize(img: Tensor, size: List[int], interpolation: int = 2) -> Tensor:
:math:`\left(\text{size} \times \frac{\text{height}}{\text{width}}, \text{size}\right)`.
In torchscript mode padding as a single int is not supported, use a tuple or
list of length 1: ``[size, ]``.
interpolation (int, optional): Desired interpolation. Default is bilinear (=2). Other supported values:
nearest(=0) and bicubic(=3).
interpolation (str): Desired interpolation. Default is "bilinear". Other supported values:
"nearest" and "bicubic".
Returns:
Tensor: Resized image.
......@@ -785,16 +785,10 @@ def resize(img: Tensor, size: List[int], interpolation: int = 2) -> Tensor:
if not isinstance(size, (int, tuple, list)):
raise TypeError("Got inappropriate size arg")
if not isinstance(interpolation, int):
if not isinstance(interpolation, str):
raise TypeError("Got inappropriate interpolation arg")
_interpolation_modes = {
0: "nearest",
2: "bilinear",
3: "bicubic",
}
if interpolation not in _interpolation_modes:
if interpolation not in ["nearest", "bilinear", "bicubic"]:
raise ValueError("This interpolation mode is unsupported with Tensor input")
if isinstance(size, tuple):
......@@ -822,16 +816,14 @@ def resize(img: Tensor, size: List[int], interpolation: int = 2) -> Tensor:
if (w <= h and w == size_w) or (h <= w and h == size_h):
return img
mode = _interpolation_modes[interpolation]
img, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [torch.float32, torch.float64])
# Define align_corners to avoid warnings
align_corners = False if mode in ["bilinear", "bicubic"] else None
align_corners = False if interpolation in ["bilinear", "bicubic"] else None
img = interpolate(img, size=[size_h, size_w], mode=mode, align_corners=align_corners)
img = interpolate(img, size=[size_h, size_w], mode=interpolation, align_corners=align_corners)
if mode == "bicubic" and out_dtype == torch.uint8:
if interpolation == "bicubic" and out_dtype == torch.uint8:
img = img.clamp(min=0, max=255)
img = _cast_squeeze_out(img, need_cast=need_cast, need_squeeze=need_squeeze, out_dtype=out_dtype)
......@@ -842,9 +834,9 @@ def resize(img: Tensor, size: List[int], interpolation: int = 2) -> Tensor:
def _assert_grid_transform_inputs(
img: Tensor,
matrix: Optional[List[float]],
resample: int,
fillcolor: Optional[int],
_interpolation_modes: Dict[int, str],
interpolation: str,
fill: Optional[int],
supported_interpolation_modes: List[str],
coeffs: Optional[List[float]] = None,
):
if not (isinstance(img, torch.Tensor) and _is_tensor_a_torch_image(img)):
......@@ -859,11 +851,11 @@ def _assert_grid_transform_inputs(
if coeffs is not None and len(coeffs) != 8:
raise ValueError("Argument coeffs should have 8 float values")
if fillcolor is not None:
warnings.warn("Argument fill/fillcolor is not supported for Tensor input. Fill value is zero")
if fill is not None and not (isinstance(fill, (int, float)) and fill == 0):
warnings.warn("Argument fill is not supported for Tensor input. Fill value is zero")
if resample not in _interpolation_modes:
raise ValueError("Resampling mode '{}' is unsupported with Tensor input".format(resample))
if interpolation not in supported_interpolation_modes:
raise ValueError("Interpolation mode '{}' is unsupported with Tensor input".format(interpolation))
def _cast_squeeze_in(img: Tensor, req_dtypes: List[torch.dtype]) -> Tuple[Tensor, bool, bool, torch.dtype]:
......@@ -931,7 +923,7 @@ def _gen_affine_grid(
def affine(
img: Tensor, matrix: List[float], resample: int = 0, fillcolor: Optional[int] = None
img: Tensor, matrix: List[float], interpolation: str = "nearest", fill: Optional[int] = None
) -> Tensor:
"""PRIVATE METHOD. Apply affine transformation on the Tensor image keeping image center invariant.
......@@ -943,28 +935,21 @@ def affine(
Args:
img (Tensor): image to be rotated.
matrix (list of floats): list of 6 float values representing inverse matrix for affine transformation.
resample (int, optional): An optional resampling filter. Default is nearest (=0). Other supported values:
bilinear(=2).
fillcolor (int, optional): this option is not supported for Tensor input. Fill value for the area outside the
interpolation (str): An optional resampling filter. Default is "nearest". Other supported values: "bilinear".
fill (int, optional): this option is not supported for Tensor input. Fill value for the area outside the
transform in the output image is always 0.
Returns:
Tensor: Transformed image.
"""
_interpolation_modes = {
0: "nearest",
2: "bilinear",
}
_assert_grid_transform_inputs(img, matrix, resample, fillcolor, _interpolation_modes)
_assert_grid_transform_inputs(img, matrix, interpolation, fill, ["nearest", "bilinear"])
dtype = img.dtype if torch.is_floating_point(img) else torch.float32
theta = torch.tensor(matrix, dtype=dtype, device=img.device).reshape(1, 2, 3)
shape = img.shape
# grid will be generated on the same device as theta and img
grid = _gen_affine_grid(theta, w=shape[-1], h=shape[-2], ow=shape[-1], oh=shape[-2])
mode = _interpolation_modes[resample]
return _apply_grid_transform(img, grid, mode)
return _apply_grid_transform(img, grid, interpolation)
def _compute_output_size(matrix: List[float], w: int, h: int) -> Tuple[int, int]:
......@@ -993,7 +978,8 @@ def _compute_output_size(matrix: List[float], w: int, h: int) -> Tuple[int, int]
def rotate(
img: Tensor, matrix: List[float], resample: int = 0, expand: bool = False, fill: Optional[int] = None
img: Tensor, matrix: List[float], interpolation: str = "nearest",
expand: bool = False, fill: Optional[int] = None
) -> Tensor:
"""PRIVATE METHOD. Rotate the Tensor image by angle.
......@@ -1006,8 +992,7 @@ def rotate(
img (Tensor): image to be rotated.
matrix (list of floats): list of 6 float values representing inverse matrix for rotation transformation.
Translation part (``matrix[2]`` and ``matrix[5]``) should be in pixel coordinates.
resample (int, optional): An optional resampling filter. Default is nearest (=0). Other supported values:
bilinear(=2).
interpolation (str): An optional resampling filter. Default is "nearest". Other supported values: "bilinear".
expand (bool, optional): Optional expansion flag.
If true, expands the output image to make it large enough to hold the entire rotated image.
If false or omitted, make the output image the same size as the input image.
......@@ -1021,21 +1006,14 @@ def rotate(
.. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters
"""
_interpolation_modes = {
0: "nearest",
2: "bilinear",
}
_assert_grid_transform_inputs(img, matrix, resample, fill, _interpolation_modes)
_assert_grid_transform_inputs(img, matrix, interpolation, fill, ["nearest", "bilinear"])
w, h = img.shape[-1], img.shape[-2]
ow, oh = _compute_output_size(matrix, w, h) if expand else (w, h)
dtype = img.dtype if torch.is_floating_point(img) else torch.float32
theta = torch.tensor(matrix, dtype=dtype, device=img.device).reshape(1, 2, 3)
# grid will be generated on the same device as theta and img
grid = _gen_affine_grid(theta, w=w, h=h, ow=ow, oh=oh)
mode = _interpolation_modes[resample]
return _apply_grid_transform(img, grid, mode)
return _apply_grid_transform(img, grid, interpolation)
def _perspective_grid(coeffs: List[float], ow: int, oh: int, dtype: torch.dtype, device: torch.device):
......@@ -1072,7 +1050,7 @@ def _perspective_grid(coeffs: List[float], ow: int, oh: int, dtype: torch.dtype,
def perspective(
img: Tensor, perspective_coeffs: List[float], interpolation: int = 2, fill: Optional[int] = None
img: Tensor, perspective_coeffs: List[float], interpolation: str = "bilinear", fill: Optional[int] = None
) -> Tensor:
"""PRIVATE METHOD. Perform perspective transform of the given Tensor image.
......@@ -1084,7 +1062,7 @@ def perspective(
Args:
img (Tensor): Image to be transformed.
perspective_coeffs (list of float): perspective transformation coefficients.
interpolation (int): Interpolation type. Default, ``PIL.Image.BILINEAR``.
interpolation (str): Interpolation type. Default, "bilinear".
fill (n-tuple or int or float): this option is not supported for Tensor input. Fill value for the area
outside the transform in the output image is always 0.
......@@ -1094,26 +1072,19 @@ def perspective(
if not (isinstance(img, torch.Tensor) and _is_tensor_a_torch_image(img)):
raise TypeError('Input img should be Tensor Image')
_interpolation_modes = {
0: "nearest",
2: "bilinear",
}
_assert_grid_transform_inputs(
img,
matrix=None,
resample=interpolation,
fillcolor=fill,
_interpolation_modes=_interpolation_modes,
interpolation=interpolation,
fill=fill,
supported_interpolation_modes=["nearest", "bilinear"],
coeffs=perspective_coeffs
)
ow, oh = img.shape[-1], img.shape[-2]
dtype = img.dtype if torch.is_floating_point(img) else torch.float32
grid = _perspective_grid(perspective_coeffs, ow=ow, oh=oh, dtype=dtype, device=img.device)
mode = _interpolation_modes[interpolation]
return _apply_grid_transform(img, grid, mode)
return _apply_grid_transform(img, grid, interpolation)
def _get_gaussian_kernel1d(kernel_size: int, sigma: float) -> Tensor:
......
torchvision/transforms/transforms.py
View file @ 0c445130
......@@ -6,7 +6,6 @@ from collections.abc import Sequence
from typing import Tuple, List, Optional
import torch
from PIL import Image
from torch import Tensor
try:
......@@ -15,21 +14,14 @@ except ImportError:
accimage = None
from . import functional as F
from .functional import InterpolationModes, _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"]
_pil_interpolation_to_str = {
Image.NEAREST: 'PIL.Image.NEAREST',
Image.BILINEAR: 'PIL.Image.BILINEAR',
Image.BICUBIC: 'PIL.Image.BICUBIC',
Image.LANCZOS: 'PIL.Image.LANCZOS',
Image.HAMMING: 'PIL.Image.HAMMING',
Image.BOX: 'PIL.Image.BOX',
}
"RandomPerspective", "RandomErasing", "GaussianBlur", "InterpolationModes"]
class Compose:
......@@ -242,18 +234,30 @@ 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 (int, optional): Desired interpolation enum defined by `filters`_.
Default is ``PIL.Image.BILINEAR``. If input is Tensor, only ``PIL.Image.NEAREST``, ``PIL.Image.BILINEAR``
and ``PIL.Image.BICUBIC`` are supported.
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.
For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
"""
def __init__(self, size, interpolation=Image.BILINEAR):
def __init__(self, size, interpolation=InterpolationModes.BILINEAR):
super().__init__()
if not isinstance(size, (int, Sequence)):
raise TypeError("Size should be int or sequence. Got {}".format(type(size)))
if isinstance(size, Sequence) and len(size) not in (1, 2):
raise ValueError("If size is a sequence, it should have 1 or 2 values")
self.size = size
# 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."
)
interpolation = _interpolation_modes_from_int(interpolation)
self.interpolation = interpolation
def forward(self, img):
......@@ -267,7 +271,7 @@ class Resize(torch.nn.Module):
return F.resize(img, self.size, self.interpolation)
def __repr__(self):
interpolate_str = _pil_interpolation_to_str[self.interpolation]
interpolate_str = self.interpolation.value
return self.__class__.__name__ + '(size={0}, interpolation={1})'.format(self.size, interpolate_str)
......@@ -659,18 +663,28 @@ 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 (int): Interpolation type. If input is Tensor, only ``PIL.Image.NEAREST`` and
``PIL.Image.BILINEAR`` are supported. Default, ``PIL.Image.BILINEAR`` for PIL images and Tensors.
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.
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.
This option is only available for ``pillow>=5.0.0``. This option is not supported for Tensor
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=Image.BILINEAR, fill=0):
def __init__(self, distortion_scale=0.5, p=0.5, interpolation=InterpolationModes.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."
)
interpolation = _interpolation_modes_from_int(interpolation)
self.interpolation = interpolation
self.distortion_scale = distortion_scale
self.fill = fill
......@@ -744,12 +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 (int): Desired interpolation enum defined by `filters`_.
Default is ``PIL.Image.BILINEAR``. If input is Tensor, only ``PIL.Image.NEAREST``, ``PIL.Image.BILINEAR``
and ``PIL.Image.BICUBIC`` are supported.
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.
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=Image.BILINEAR):
def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.), interpolation=InterpolationModes.BILINEAR):
super().__init__()
self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
......@@ -760,6 +777,14 @@ class RandomResizedCrop(torch.nn.Module):
if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
warnings.warn("Scale and ratio should be of kind (min, max)")
# 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."
)
interpolation = _interpolation_modes_from_int(interpolation)
self.interpolation = interpolation
self.scale = scale
self.ratio = ratio
......@@ -824,7 +849,7 @@ class RandomResizedCrop(torch.nn.Module):
return F.resized_crop(img, i, j, h, w, self.size, self.interpolation)
def __repr__(self):
interpolate_str = _pil_interpolation_to_str[self.interpolation]
interpolate_str = self.interpolation.value
format_string = self.__class__.__name__ + '(size={0}'.format(self.size)
format_string += ', scale={0}'.format(tuple(round(s, 4) for s in self.scale))
format_string += ', ratio={0}'.format(tuple(round(r, 4) for r in self.ratio))
......@@ -1122,9 +1147,10 @@ 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).
resample (int, optional): An optional resampling filter. See `filters`_ for more information.
If omitted, or if the image has mode "1" or "P", it is set to PIL.Image.NEAREST.
If input is Tensor, only ``PIL.Image.NEAREST`` and ``PIL.Image.BILINEAR`` are supported.
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.
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.
If false or omitted, make the output image the same size as the input image.
......@@ -1136,13 +1162,31 @@ class RandomRotation(torch.nn.Module):
Defaults to 0 for all bands. This option is only available for Pillow>=5.2.0.
This option is not supported for Tensor input. Fill value for the area outside the transform in the output
image is always 0.
resample (int, optional): deprecated argument and will be removed since v0.10.0.
Please use `arg`:interpolation: instead.
.. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters
"""
def __init__(self, degrees, resample=False, expand=False, center=None, fill=None):
def __init__(
self, degrees, interpolation=InterpolationModes.NEAREST, expand=False, center=None, fill=None, resample=None
):
super().__init__()
if resample is not None:
warnings.warn(
"Argument resample is deprecated and will be removed since v0.10.0. Please, use interpolation instead"
)
interpolation = _interpolation_modes_from_int(resample)
# 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."
)
interpolation = _interpolation_modes_from_int(interpolation)
self.degrees = _setup_angle(degrees, name="degrees", req_sizes=(2, ))
if center is not None:
......@@ -1150,7 +1194,7 @@ class RandomRotation(torch.nn.Module):
self.center = center
self.resample = resample
self.resample = self.interpolation = interpolation
self.expand = expand
self.fill = fill
......@@ -1173,11 +1217,12 @@ class RandomRotation(torch.nn.Module):
PIL Image or Tensor: Rotated image.
"""
angle = self.get_params(self.degrees)
return F.rotate(img, angle, self.resample, self.expand, self.center, self.fill)
return F.rotate(img, angle, self.interpolation, self.expand, self.center, self.fill)
def __repr__(self):
interpolate_str = self.interpolation.value
format_string = self.__class__.__name__ + '(degrees={0}'.format(self.degrees)
format_string += ', resample={0}'.format(self.resample)
format_string += ', interpolation={0}'.format(interpolate_str)
format_string += ', expand={0}'.format(self.expand)
if self.center is not None:
format_string += ', center={0}'.format(self.center)
......@@ -1208,19 +1253,47 @@ 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.
resample (int, optional): An optional resampling filter. See `filters`_ for more information.
If omitted, or if the image has mode "1" or "P", it is set to ``PIL.Image.NEAREST``.
If input is Tensor, only ``PIL.Image.NEAREST`` and ``PIL.Image.BILINEAR`` are supported.
fillcolor (tuple or int): Optional fill color (Tuple for RGB Image and int for grayscale) for the area
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.
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
input. Fill value for the area outside the transform in the output image is always 0.
fillcolor (tuple or int, optional): deprecated argument and will be removed since v0.10.0.
Please use `arg`:fill: instead.
resample (int, optional): deprecated argument and will be removed since v0.10.0.
Please use `arg`:interpolation: instead.
.. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters
"""
def __init__(self, degrees, translate=None, scale=None, shear=None, resample=0, fillcolor=0):
def __init__(
self, degrees, translate=None, scale=None, shear=None, interpolation=InterpolationModes.NEAREST, fill=0,
fillcolor=None, resample=None
):
super().__init__()
if resample is not None:
warnings.warn(
"Argument resample is deprecated and will be removed since v0.10.0. Please, use interpolation instead"
)
interpolation = _interpolation_modes_from_int(resample)
# 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."
)
interpolation = _interpolation_modes_from_int(interpolation)
if fillcolor is not None:
warnings.warn(
"Argument fillcolor is deprecated and will be removed since v0.10.0. Please, use fill instead"
)
fill = fillcolor
self.degrees = _setup_angle(degrees, name="degrees", req_sizes=(2, ))
if translate is not None:
......@@ -1242,8 +1315,8 @@ class RandomAffine(torch.nn.Module):
else:
self.shear = shear
self.resample = resample
self.fillcolor = fillcolor
self.resample = self.interpolation = interpolation
self.fillcolor = self.fill = fill
@staticmethod
def get_params(
......@@ -1294,7 +1367,7 @@ class RandomAffine(torch.nn.Module):
img_size = F._get_image_size(img)
ret = self.get_params(self.degrees, self.translate, self.scale, self.shear, img_size)
return F.affine(img, *ret, resample=self.resample, fillcolor=self.fillcolor)
return F.affine(img, *ret, interpolation=self.interpolation, fill=self.fill)
def __repr__(self):
s = '{name}(degrees={degrees}'
......@@ -1304,13 +1377,13 @@ class RandomAffine(torch.nn.Module):
s += ', scale={scale}'
if self.shear is not None:
s += ', shear={shear}'
if self.resample > 0:
s += ', resample={resample}'
if self.fillcolor != 0:
s += ', fillcolor={fillcolor}'
if self.interpolation != InterpolationModes.NEAREST:
s += ', interpolation={interpolation}'
if self.fill != 0:
s += ', fill={fill}'
s += ')'
d = dict(self.__dict__)
d['resample'] = _pil_interpolation_to_str[d['resample']]
d['interpolation'] = self.interpolation.value
return s.format(name=self.__class__.__name__, **d)
......
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