Source code for torchvision.transforms.v2._auto_augment
import math
from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
import PIL.Image
import torch
from torch.utils._pytree import tree_flatten, tree_unflatten, TreeSpec
from torchvision import transforms as _transforms, tv_tensors
from torchvision.transforms import _functional_tensor as _FT
from torchvision.transforms.v2 import AutoAugmentPolicy, functional as F, InterpolationMode, Transform
from torchvision.transforms.v2.functional._geometry import _check_interpolation
from torchvision.transforms.v2.functional._meta import get_size
from torchvision.transforms.v2.functional._utils import _FillType, _FillTypeJIT
from ._utils import _get_fill, _setup_fill_arg, check_type, is_pure_tensor
ImageOrVideo = Union[torch.Tensor, PIL.Image.Image, tv_tensors.Image, tv_tensors.Video]
class _AutoAugmentBase(Transform):
def __init__(
self,
*,
interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = None,
) -> None:
super().__init__()
self.interpolation = _check_interpolation(interpolation)
self.fill = fill
self._fill = _setup_fill_arg(fill)
def _extract_params_for_v1_transform(self) -> Dict[str, Any]:
params = super()._extract_params_for_v1_transform()
if isinstance(params["fill"], dict):
raise ValueError(f"{type(self).__name__}() can not be scripted for when `fill` is a dictionary.")
return params
def _get_random_item(self, dct: Dict[str, Tuple[Callable, bool]]) -> Tuple[str, Tuple[Callable, bool]]:
keys = tuple(dct.keys())
key = keys[int(torch.randint(len(keys), ()))]
return key, dct[key]
def _flatten_and_extract_image_or_video(
self,
inputs: Any,
unsupported_types: Tuple[Type, ...] = (tv_tensors.BoundingBoxes, tv_tensors.Mask),
) -> Tuple[Tuple[List[Any], TreeSpec, int], ImageOrVideo]:
flat_inputs, spec = tree_flatten(inputs if len(inputs) > 1 else inputs[0])
needs_transform_list = self._needs_transform_list(flat_inputs)
image_or_videos = []
for idx, (inpt, needs_transform) in enumerate(zip(flat_inputs, needs_transform_list)):
if needs_transform and check_type(
inpt,
(
tv_tensors.Image,
PIL.Image.Image,
is_pure_tensor,
tv_tensors.Video,
),
):
image_or_videos.append((idx, inpt))
elif isinstance(inpt, unsupported_types):
raise TypeError(f"Inputs of type {type(inpt).__name__} are not supported by {type(self).__name__}()")
if not image_or_videos:
raise TypeError("Found no image in the sample.")
if len(image_or_videos) > 1:
raise TypeError(
f"Auto augment transformations are only properly defined for a single image or video, "
f"but found {len(image_or_videos)}."
)
idx, image_or_video = image_or_videos[0]
return (flat_inputs, spec, idx), image_or_video
def _unflatten_and_insert_image_or_video(
self,
flat_inputs_with_spec: Tuple[List[Any], TreeSpec, int],
image_or_video: ImageOrVideo,
) -> Any:
flat_inputs, spec, idx = flat_inputs_with_spec
flat_inputs[idx] = image_or_video
return tree_unflatten(flat_inputs, spec)
def _apply_image_or_video_transform(
self,
image: ImageOrVideo,
transform_id: str,
magnitude: float,
interpolation: Union[InterpolationMode, int],
fill: Dict[Union[Type, str], _FillTypeJIT],
) -> ImageOrVideo:
fill_ = _get_fill(fill, type(image))
if transform_id == "Identity":
return image
elif transform_id == "ShearX":
# magnitude should be arctan(magnitude)
# official autoaug: (1, level, 0, 0, 1, 0)
# https://github.com/tensorflow/models/blob/dd02069717128186b88afa8d857ce57d17957f03/research/autoaugment/augmentation_transforms.py#L290
# compared to
# torchvision: (1, tan(level), 0, 0, 1, 0)
# https://github.com/pytorch/vision/blob/0c2373d0bba3499e95776e7936e207d8a1676e65/torchvision/transforms/functional.py#L976
return F.affine(
image,
angle=0.0,
translate=[0, 0],
scale=1.0,
shear=[math.degrees(math.atan(magnitude)), 0.0],
interpolation=interpolation,
fill=fill_,
center=[0, 0],
)
elif transform_id == "ShearY":
# magnitude should be arctan(magnitude)
# See above
return F.affine(
image,
angle=0.0,
translate=[0, 0],
scale=1.0,
shear=[0.0, math.degrees(math.atan(magnitude))],
interpolation=interpolation,
fill=fill_,
center=[0, 0],
)
elif transform_id == "TranslateX":
return F.affine(
image,
angle=0.0,
translate=[int(magnitude), 0],
scale=1.0,
interpolation=interpolation,
shear=[0.0, 0.0],
fill=fill_,
)
elif transform_id == "TranslateY":
return F.affine(
image,
angle=0.0,
translate=[0, int(magnitude)],
scale=1.0,
interpolation=interpolation,
shear=[0.0, 0.0],
fill=fill_,
)
elif transform_id == "Rotate":
return F.rotate(image, angle=magnitude, interpolation=interpolation, fill=fill_)
elif transform_id == "Brightness":
return F.adjust_brightness(image, brightness_factor=1.0 + magnitude)
elif transform_id == "Color":
return F.adjust_saturation(image, saturation_factor=1.0 + magnitude)
elif transform_id == "Contrast":
return F.adjust_contrast(image, contrast_factor=1.0 + magnitude)
elif transform_id == "Sharpness":
return F.adjust_sharpness(image, sharpness_factor=1.0 + magnitude)
elif transform_id == "Posterize":
return F.posterize(image, bits=int(magnitude))
elif transform_id == "Solarize":
bound = _FT._max_value(image.dtype) if isinstance(image, torch.Tensor) else 255.0
return F.solarize(image, threshold=bound * magnitude)
elif transform_id == "AutoContrast":
return F.autocontrast(image)
elif transform_id == "Equalize":
return F.equalize(image)
elif transform_id == "Invert":
return F.invert(image)
else:
raise ValueError(f"No transform available for {transform_id}")
[docs]class AutoAugment(_AutoAugmentBase):
r"""AutoAugment data augmentation method based on
`"AutoAugment: Learning Augmentation Strategies from Data" <https://arxiv.org/pdf/1805.09501.pdf>`_.
This transformation works on images and videos only.
If the input is :class:`torch.Tensor`, it should be of type ``torch.uint8``, and it is expected
to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
If img is PIL Image, it is expected to be in mode "L" or "RGB".
Args:
policy (AutoAugmentPolicy, optional): Desired policy enum defined by
:class:`torchvision.transforms.autoaugment.AutoAugmentPolicy`. Default is ``AutoAugmentPolicy.IMAGENET``.
interpolation (InterpolationMode, optional): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
fill (sequence or number, optional): Pixel fill value for the area outside the transformed
image. If given a number, the value is used for all bands respectively.
"""
_v1_transform_cls = _transforms.AutoAugment
_AUGMENTATION_SPACE = {
"ShearX": (lambda num_bins, height, width: torch.linspace(0.0, 0.3, num_bins), True),
"ShearY": (lambda num_bins, height, width: torch.linspace(0.0, 0.3, num_bins), True),
"TranslateX": (
lambda num_bins, height, width: torch.linspace(0.0, 150.0 / 331.0 * width, num_bins),
True,
),
"TranslateY": (
lambda num_bins, height, width: torch.linspace(0.0, 150.0 / 331.0 * height, num_bins),
True,
),
"Rotate": (lambda num_bins, height, width: torch.linspace(0.0, 30.0, num_bins), True),
"Brightness": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
"Color": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
"Contrast": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
"Sharpness": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
"Posterize": (
lambda num_bins, height, width: (8 - (torch.arange(num_bins) / ((num_bins - 1) / 4))).round().int(),
False,
),
"Solarize": (lambda num_bins, height, width: torch.linspace(1.0, 0.0, num_bins), False),
"AutoContrast": (lambda num_bins, height, width: None, False),
"Equalize": (lambda num_bins, height, width: None, False),
"Invert": (lambda num_bins, height, width: None, False),
}
def __init__(
self,
policy: AutoAugmentPolicy = AutoAugmentPolicy.IMAGENET,
interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = None,
) -> None:
super().__init__(interpolation=interpolation, fill=fill)
self.policy = policy
self._policies = self._get_policies(policy)
def _get_policies(
self, policy: AutoAugmentPolicy
) -> List[Tuple[Tuple[str, float, Optional[int]], Tuple[str, float, Optional[int]]]]:
if policy == AutoAugmentPolicy.IMAGENET:
return [
(("Posterize", 0.4, 8), ("Rotate", 0.6, 9)),
(("Solarize", 0.6, 5), ("AutoContrast", 0.6, None)),
(("Equalize", 0.8, None), ("Equalize", 0.6, None)),
(("Posterize", 0.6, 7), ("Posterize", 0.6, 6)),
(("Equalize", 0.4, None), ("Solarize", 0.2, 4)),
(("Equalize", 0.4, None), ("Rotate", 0.8, 8)),
(("Solarize", 0.6, 3), ("Equalize", 0.6, None)),
(("Posterize", 0.8, 5), ("Equalize", 1.0, None)),
(("Rotate", 0.2, 3), ("Solarize", 0.6, 8)),
(("Equalize", 0.6, None), ("Posterize", 0.4, 6)),
(("Rotate", 0.8, 8), ("Color", 0.4, 0)),
(("Rotate", 0.4, 9), ("Equalize", 0.6, None)),
(("Equalize", 0.0, None), ("Equalize", 0.8, None)),
(("Invert", 0.6, None), ("Equalize", 1.0, None)),
(("Color", 0.6, 4), ("Contrast", 1.0, 8)),
(("Rotate", 0.8, 8), ("Color", 1.0, 2)),
(("Color", 0.8, 8), ("Solarize", 0.8, 7)),
(("Sharpness", 0.4, 7), ("Invert", 0.6, None)),
(("ShearX", 0.6, 5), ("Equalize", 1.0, None)),
(("Color", 0.4, 0), ("Equalize", 0.6, None)),
(("Equalize", 0.4, None), ("Solarize", 0.2, 4)),
(("Solarize", 0.6, 5), ("AutoContrast", 0.6, None)),
(("Invert", 0.6, None), ("Equalize", 1.0, None)),
(("Color", 0.6, 4), ("Contrast", 1.0, 8)),
(("Equalize", 0.8, None), ("Equalize", 0.6, None)),
]
elif policy == AutoAugmentPolicy.CIFAR10:
return [
(("Invert", 0.1, None), ("Contrast", 0.2, 6)),
(("Rotate", 0.7, 2), ("TranslateX", 0.3, 9)),
(("Sharpness", 0.8, 1), ("Sharpness", 0.9, 3)),
(("ShearY", 0.5, 8), ("TranslateY", 0.7, 9)),
(("AutoContrast", 0.5, None), ("Equalize", 0.9, None)),
(("ShearY", 0.2, 7), ("Posterize", 0.3, 7)),
(("Color", 0.4, 3), ("Brightness", 0.6, 7)),
(("Sharpness", 0.3, 9), ("Brightness", 0.7, 9)),
(("Equalize", 0.6, None), ("Equalize", 0.5, None)),
(("Contrast", 0.6, 7), ("Sharpness", 0.6, 5)),
(("Color", 0.7, 7), ("TranslateX", 0.5, 8)),
(("Equalize", 0.3, None), ("AutoContrast", 0.4, None)),
(("TranslateY", 0.4, 3), ("Sharpness", 0.2, 6)),
(("Brightness", 0.9, 6), ("Color", 0.2, 8)),
(("Solarize", 0.5, 2), ("Invert", 0.0, None)),
(("Equalize", 0.2, None), ("AutoContrast", 0.6, None)),
(("Equalize", 0.2, None), ("Equalize", 0.6, None)),
(("Color", 0.9, 9), ("Equalize", 0.6, None)),
(("AutoContrast", 0.8, None), ("Solarize", 0.2, 8)),
(("Brightness", 0.1, 3), ("Color", 0.7, 0)),
(("Solarize", 0.4, 5), ("AutoContrast", 0.9, None)),
(("TranslateY", 0.9, 9), ("TranslateY", 0.7, 9)),
(("AutoContrast", 0.9, None), ("Solarize", 0.8, 3)),
(("Equalize", 0.8, None), ("Invert", 0.1, None)),
(("TranslateY", 0.7, 9), ("AutoContrast", 0.9, None)),
]
elif policy == AutoAugmentPolicy.SVHN:
return [
(("ShearX", 0.9, 4), ("Invert", 0.2, None)),
(("ShearY", 0.9, 8), ("Invert", 0.7, None)),
(("Equalize", 0.6, None), ("Solarize", 0.6, 6)),
(("Invert", 0.9, None), ("Equalize", 0.6, None)),
(("Equalize", 0.6, None), ("Rotate", 0.9, 3)),
(("ShearX", 0.9, 4), ("AutoContrast", 0.8, None)),
(("ShearY", 0.9, 8), ("Invert", 0.4, None)),
(("ShearY", 0.9, 5), ("Solarize", 0.2, 6)),
(("Invert", 0.9, None), ("AutoContrast", 0.8, None)),
(("Equalize", 0.6, None), ("Rotate", 0.9, 3)),
(("ShearX", 0.9, 4), ("Solarize", 0.3, 3)),
(("ShearY", 0.8, 8), ("Invert", 0.7, None)),
(("Equalize", 0.9, None), ("TranslateY", 0.6, 6)),
(("Invert", 0.9, None), ("Equalize", 0.6, None)),
(("Contrast", 0.3, 3), ("Rotate", 0.8, 4)),
(("Invert", 0.8, None), ("TranslateY", 0.0, 2)),
(("ShearY", 0.7, 6), ("Solarize", 0.4, 8)),
(("Invert", 0.6, None), ("Rotate", 0.8, 4)),
(("ShearY", 0.3, 7), ("TranslateX", 0.9, 3)),
(("ShearX", 0.1, 6), ("Invert", 0.6, None)),
(("Solarize", 0.7, 2), ("TranslateY", 0.6, 7)),
(("ShearY", 0.8, 4), ("Invert", 0.8, None)),
(("ShearX", 0.7, 9), ("TranslateY", 0.8, 3)),
(("ShearY", 0.8, 5), ("AutoContrast", 0.7, None)),
(("ShearX", 0.7, 2), ("Invert", 0.1, None)),
]
else:
raise ValueError(f"The provided policy {policy} is not recognized.")
[docs] def forward(self, *inputs: Any) -> Any:
flat_inputs_with_spec, image_or_video = self._flatten_and_extract_image_or_video(inputs)
height, width = get_size(image_or_video)
policy = self._policies[int(torch.randint(len(self._policies), ()))]
for transform_id, probability, magnitude_idx in policy:
if not torch.rand(()) <= probability:
continue
magnitudes_fn, signed = self._AUGMENTATION_SPACE[transform_id]
magnitudes = magnitudes_fn(10, height, width)
if magnitudes is not None:
magnitude = float(magnitudes[magnitude_idx])
if signed and torch.rand(()) <= 0.5:
magnitude *= -1
else:
magnitude = 0.0
image_or_video = self._apply_image_or_video_transform(
image_or_video, transform_id, magnitude, interpolation=self.interpolation, fill=self._fill
)
return self._unflatten_and_insert_image_or_video(flat_inputs_with_spec, image_or_video)
[docs]class RandAugment(_AutoAugmentBase):
r"""RandAugment data augmentation method based on
`"RandAugment: Practical automated data augmentation with a reduced search space"
<https://arxiv.org/abs/1909.13719>`_.
This transformation works on images and videos only.
If the input is :class:`torch.Tensor`, it should be of type ``torch.uint8``, and it is expected
to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
If img is PIL Image, it is expected to be in mode "L" or "RGB".
Args:
num_ops (int, optional): Number of augmentation transformations to apply sequentially.
magnitude (int, optional): Magnitude for all the transformations.
num_magnitude_bins (int, optional): The number of different magnitude values.
interpolation (InterpolationMode, optional): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
fill (sequence or number, optional): Pixel fill value for the area outside the transformed
image. If given a number, the value is used for all bands respectively.
"""
_v1_transform_cls = _transforms.RandAugment
_AUGMENTATION_SPACE = {
"Identity": (lambda num_bins, height, width: None, False),
"ShearX": (lambda num_bins, height, width: torch.linspace(0.0, 0.3, num_bins), True),
"ShearY": (lambda num_bins, height, width: torch.linspace(0.0, 0.3, num_bins), True),
"TranslateX": (
lambda num_bins, height, width: torch.linspace(0.0, 150.0 / 331.0 * width, num_bins),
True,
),
"TranslateY": (
lambda num_bins, height, width: torch.linspace(0.0, 150.0 / 331.0 * height, num_bins),
True,
),
"Rotate": (lambda num_bins, height, width: torch.linspace(0.0, 30.0, num_bins), True),
"Brightness": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
"Color": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
"Contrast": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
"Sharpness": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
"Posterize": (
lambda num_bins, height, width: (8 - (torch.arange(num_bins) / ((num_bins - 1) / 4))).round().int(),
False,
),
"Solarize": (lambda num_bins, height, width: torch.linspace(1.0, 0.0, num_bins), False),
"AutoContrast": (lambda num_bins, height, width: None, False),
"Equalize": (lambda num_bins, height, width: None, False),
}
def __init__(
self,
num_ops: int = 2,
magnitude: int = 9,
num_magnitude_bins: int = 31,
interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = None,
) -> None:
super().__init__(interpolation=interpolation, fill=fill)
self.num_ops = num_ops
self.magnitude = magnitude
self.num_magnitude_bins = num_magnitude_bins
[docs] def forward(self, *inputs: Any) -> Any:
flat_inputs_with_spec, image_or_video = self._flatten_and_extract_image_or_video(inputs)
height, width = get_size(image_or_video)
for _ in range(self.num_ops):
transform_id, (magnitudes_fn, signed) = self._get_random_item(self._AUGMENTATION_SPACE)
magnitudes = magnitudes_fn(self.num_magnitude_bins, height, width)
if magnitudes is not None:
magnitude = float(magnitudes[self.magnitude])
if signed and torch.rand(()) <= 0.5:
magnitude *= -1
else:
magnitude = 0.0
image_or_video = self._apply_image_or_video_transform(
image_or_video, transform_id, magnitude, interpolation=self.interpolation, fill=self._fill
)
return self._unflatten_and_insert_image_or_video(flat_inputs_with_spec, image_or_video)
[docs]class TrivialAugmentWide(_AutoAugmentBase):
r"""Dataset-independent data-augmentation with TrivialAugment Wide, as described in
`"TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation" <https://arxiv.org/abs/2103.10158>`_.
This transformation works on images and videos only.
If the input is :class:`torch.Tensor`, it should be of type ``torch.uint8``, and it is expected
to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
If img is PIL Image, it is expected to be in mode "L" or "RGB".
Args:
num_magnitude_bins (int, optional): The number of different magnitude values.
interpolation (InterpolationMode, optional): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
fill (sequence or number, optional): Pixel fill value for the area outside the transformed
image. If given a number, the value is used for all bands respectively.
"""
_v1_transform_cls = _transforms.TrivialAugmentWide
_AUGMENTATION_SPACE = {
"Identity": (lambda num_bins, height, width: None, False),
"ShearX": (lambda num_bins, height, width: torch.linspace(0.0, 0.99, num_bins), True),
"ShearY": (lambda num_bins, height, width: torch.linspace(0.0, 0.99, num_bins), True),
"TranslateX": (lambda num_bins, height, width: torch.linspace(0.0, 32.0, num_bins), True),
"TranslateY": (lambda num_bins, height, width: torch.linspace(0.0, 32.0, num_bins), True),
"Rotate": (lambda num_bins, height, width: torch.linspace(0.0, 135.0, num_bins), True),
"Brightness": (lambda num_bins, height, width: torch.linspace(0.0, 0.99, num_bins), True),
"Color": (lambda num_bins, height, width: torch.linspace(0.0, 0.99, num_bins), True),
"Contrast": (lambda num_bins, height, width: torch.linspace(0.0, 0.99, num_bins), True),
"Sharpness": (lambda num_bins, height, width: torch.linspace(0.0, 0.99, num_bins), True),
"Posterize": (
lambda num_bins, height, width: (8 - (torch.arange(num_bins) / ((num_bins - 1) / 6))).round().int(),
False,
),
"Solarize": (lambda num_bins, height, width: torch.linspace(1.0, 0.0, num_bins), False),
"AutoContrast": (lambda num_bins, height, width: None, False),
"Equalize": (lambda num_bins, height, width: None, False),
}
def __init__(
self,
num_magnitude_bins: int = 31,
interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = None,
):
super().__init__(interpolation=interpolation, fill=fill)
self.num_magnitude_bins = num_magnitude_bins
[docs] def forward(self, *inputs: Any) -> Any:
flat_inputs_with_spec, image_or_video = self._flatten_and_extract_image_or_video(inputs)
height, width = get_size(image_or_video)
transform_id, (magnitudes_fn, signed) = self._get_random_item(self._AUGMENTATION_SPACE)
magnitudes = magnitudes_fn(self.num_magnitude_bins, height, width)
if magnitudes is not None:
magnitude = float(magnitudes[int(torch.randint(self.num_magnitude_bins, ()))])
if signed and torch.rand(()) <= 0.5:
magnitude *= -1
else:
magnitude = 0.0
image_or_video = self._apply_image_or_video_transform(
image_or_video, transform_id, magnitude, interpolation=self.interpolation, fill=self._fill
)
return self._unflatten_and_insert_image_or_video(flat_inputs_with_spec, image_or_video)
[docs]class AugMix(_AutoAugmentBase):
r"""AugMix data augmentation method based on
`"AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty" <https://arxiv.org/abs/1912.02781>`_.
This transformation works on images and videos only.
If the input is :class:`torch.Tensor`, it should be of type ``torch.uint8``, and it is expected
to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
If img is PIL Image, it is expected to be in mode "L" or "RGB".
Args:
severity (int, optional): The severity of base augmentation operators. Default is ``3``.
mixture_width (int, optional): The number of augmentation chains. Default is ``3``.
chain_depth (int, optional): The depth of augmentation chains. A negative value denotes stochastic depth sampled from the interval [1, 3].
Default is ``-1``.
alpha (float, optional): The hyperparameter for the probability distributions. Default is ``1.0``.
all_ops (bool, optional): Use all operations (including brightness, contrast, color and sharpness). Default is ``True``.
interpolation (InterpolationMode, optional): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
fill (sequence or number, optional): Pixel fill value for the area outside the transformed
image. If given a number, the value is used for all bands respectively.
"""
_v1_transform_cls = _transforms.AugMix
_PARTIAL_AUGMENTATION_SPACE = {
"ShearX": (lambda num_bins, height, width: torch.linspace(0.0, 0.3, num_bins), True),
"ShearY": (lambda num_bins, height, width: torch.linspace(0.0, 0.3, num_bins), True),
"TranslateX": (lambda num_bins, height, width: torch.linspace(0.0, width / 3.0, num_bins), True),
"TranslateY": (lambda num_bins, height, width: torch.linspace(0.0, height / 3.0, num_bins), True),
"Rotate": (lambda num_bins, height, width: torch.linspace(0.0, 30.0, num_bins), True),
"Posterize": (
lambda num_bins, height, width: (4 - (torch.arange(num_bins) / ((num_bins - 1) / 4))).round().int(),
False,
),
"Solarize": (lambda num_bins, height, width: torch.linspace(1.0, 0.0, num_bins), False),
"AutoContrast": (lambda num_bins, height, width: None, False),
"Equalize": (lambda num_bins, height, width: None, False),
}
_AUGMENTATION_SPACE: Dict[str, Tuple[Callable[[int, int, int], Optional[torch.Tensor]], bool]] = {
**_PARTIAL_AUGMENTATION_SPACE,
"Brightness": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
"Color": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
"Contrast": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
"Sharpness": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
}
def __init__(
self,
severity: int = 3,
mixture_width: int = 3,
chain_depth: int = -1,
alpha: float = 1.0,
all_ops: bool = True,
interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = None,
) -> None:
super().__init__(interpolation=interpolation, fill=fill)
self._PARAMETER_MAX = 10
if not (1 <= severity <= self._PARAMETER_MAX):
raise ValueError(f"The severity must be between [1, {self._PARAMETER_MAX}]. Got {severity} instead.")
self.severity = severity
self.mixture_width = mixture_width
self.chain_depth = chain_depth
self.alpha = alpha
self.all_ops = all_ops
def _sample_dirichlet(self, params: torch.Tensor) -> torch.Tensor:
# Must be on a separate method so that we can overwrite it in tests.
return torch._sample_dirichlet(params)
[docs] def forward(self, *inputs: Any) -> Any:
flat_inputs_with_spec, orig_image_or_video = self._flatten_and_extract_image_or_video(inputs)
height, width = get_size(orig_image_or_video)
if isinstance(orig_image_or_video, torch.Tensor):
image_or_video = orig_image_or_video
else: # isinstance(inpt, PIL.Image.Image):
image_or_video = F.pil_to_tensor(orig_image_or_video)
augmentation_space = self._AUGMENTATION_SPACE if self.all_ops else self._PARTIAL_AUGMENTATION_SPACE
orig_dims = list(image_or_video.shape)
expected_ndim = 5 if isinstance(orig_image_or_video, tv_tensors.Video) else 4
batch = image_or_video.reshape([1] * max(expected_ndim - image_or_video.ndim, 0) + orig_dims)
batch_dims = [batch.size(0)] + [1] * (batch.ndim - 1)
# Sample the beta weights for combining the original and augmented image or video. To get Beta, we use a
# Dirichlet with 2 parameters. The 1st column stores the weights of the original and the 2nd the ones of
# augmented image or video.
m = self._sample_dirichlet(
torch.tensor([self.alpha, self.alpha], device=batch.device).expand(batch_dims[0], -1)
)
# Sample the mixing weights and combine them with the ones sampled from Beta for the augmented images or videos.
combined_weights = self._sample_dirichlet(
torch.tensor([self.alpha] * self.mixture_width, device=batch.device).expand(batch_dims[0], -1)
) * m[:, 1].reshape([batch_dims[0], -1])
mix = m[:, 0].reshape(batch_dims) * batch
for i in range(self.mixture_width):
aug = batch
depth = self.chain_depth if self.chain_depth > 0 else int(torch.randint(low=1, high=4, size=(1,)).item())
for _ in range(depth):
transform_id, (magnitudes_fn, signed) = self._get_random_item(augmentation_space)
magnitudes = magnitudes_fn(self._PARAMETER_MAX, height, width)
if magnitudes is not None:
magnitude = float(magnitudes[int(torch.randint(self.severity, ()))])
if signed and torch.rand(()) <= 0.5:
magnitude *= -1
else:
magnitude = 0.0
aug = self._apply_image_or_video_transform(
aug, transform_id, magnitude, interpolation=self.interpolation, fill=self._fill
)
mix.add_(combined_weights[:, i].reshape(batch_dims) * aug)
mix = mix.reshape(orig_dims).to(dtype=image_or_video.dtype)
if isinstance(orig_image_or_video, (tv_tensors.Image, tv_tensors.Video)):
mix = tv_tensors.wrap(mix, like=orig_image_or_video)
elif isinstance(orig_image_or_video, PIL.Image.Image):
mix = F.to_pil_image(mix)
return self._unflatten_and_insert_image_or_video(flat_inputs_with_spec, mix)
