from typing import Any, Dict, Optional, Tuple, Union, cast
import torch
from torch import Tensor
from kornia.augmentation import random_generator as rg
from kornia.augmentation._2d.geometric.base import GeometricAugmentationBase2D
from kornia.constants import Resample, SamplePadding
from kornia.geometry.conversions import deg2rad
from kornia.geometry.transform import get_affine_matrix2d, warp_affine
[docs]class RandomAffine(GeometricAugmentationBase2D):
r"""Apply a random 2D affine transformation to a tensor image.
.. image:: _static/img/RandomAffine.png
The transformation is computed so that the image center is kept invariant.
Args:
degrees: 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). Set to 0 to deactivate rotations.
translate: tuple of maximum absolute fraction for horizontal
and vertical translations. For example translate=(a, b), then horizontal shift
is randomly sampled in the range -img_width * a < dx < img_width * a and vertical shift is
randomly sampled in the range -img_height * b < dy < img_height * b. Will not translate by default.
scale: scaling factor interval.
If (a, b) represents isotropic scaling, the scale is randomly sampled from the range a <= scale <= b.
If (a, b, c, d), the scale is randomly sampled from the range a <= scale_x <= b, c <= scale_y <= d.
Will keep original scale by default.
shear: Range of degrees to select from.
If float, a shear parallel to the x axis in the range (-shear, +shear) will be applied.
If (a, b), a shear parallel to the x axis in the range (-shear, +shear) will be applied.
If (a, b, c, d), then 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: resample mode from "nearest" (0) or "bilinear" (1).
padding_mode: padding mode from "zeros" (0), "border" (1) or "refection" (2).
same_on_batch: apply the same transformation across the batch.
align_corners: interpolation flag.
p: probability of applying the transformation.
keepdim: whether to keep the output shape the same as input (True) or broadcast it to the batch form (False).
Shape:
- Input: :math:`(C, H, W)` or :math:`(B, C, H, W)`, Optional: :math:`(B, 3, 3)`
- Output: :math:`(B, C, H, W)`
.. note::
This function internally uses :func:`kornia.geometry.transform.warp_affine`.
Examples:
>>> import torch
>>> rng = torch.manual_seed(0)
>>> input = torch.rand(1, 1, 3, 3)
>>> aug = RandomAffine((-15., 20.), p=1.)
>>> out = aug(input)
>>> out, aug.transform_matrix
(tensor([[[[0.3961, 0.7310, 0.1574],
[0.1781, 0.3074, 0.5648],
[0.4804, 0.8379, 0.4234]]]]), tensor([[[ 0.9923, -0.1241, 0.1319],
[ 0.1241, 0.9923, -0.1164],
[ 0.0000, 0.0000, 1.0000]]]))
>>> aug.inverse(out)
tensor([[[[0.3890, 0.6573, 0.1865],
[0.2063, 0.3074, 0.5459],
[0.3892, 0.7896, 0.4224]]]])
>>> input
tensor([[[[0.4963, 0.7682, 0.0885],
[0.1320, 0.3074, 0.6341],
[0.4901, 0.8964, 0.4556]]]])
To apply the exact augmenation again, you may take the advantage of the previous parameter state:
>>> input = torch.randn(1, 3, 32, 32)
>>> aug = RandomAffine((-15., 20.), p=1.)
>>> (aug(input) == aug(input, params=aug._params)).all()
tensor(True)
"""
def __init__(
self,
degrees: Union[Tensor, float, Tuple[float, float]],
translate: Optional[Union[Tensor, Tuple[float, float]]] = None,
scale: Optional[Union[Tensor, Tuple[float, float], Tuple[float, float, float, float]]] = None,
shear: Optional[Union[Tensor, float, Tuple[float, float]]] = None,
resample: Union[str, int, Resample] = Resample.BILINEAR.name,
same_on_batch: bool = False,
align_corners: bool = False,
padding_mode: Union[str, int, SamplePadding] = SamplePadding.ZEROS.name,
p: float = 0.5,
keepdim: bool = False,
return_transform: Optional[bool] = None,
) -> None:
super().__init__(p=p, return_transform=return_transform, same_on_batch=same_on_batch, keepdim=keepdim)
self._param_generator = cast(rg.AffineGenerator, rg.AffineGenerator(degrees, translate, scale, shear))
self.flags = dict(
resample=Resample.get(resample), padding_mode=SamplePadding.get(padding_mode), align_corners=align_corners
)
def compute_transformation(self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any]) -> Tensor:
return get_affine_matrix2d(
torch.as_tensor(params["translations"], device=input.device, dtype=input.dtype),
torch.as_tensor(params["center"], device=input.device, dtype=input.dtype),
torch.as_tensor(params["scale"], device=input.device, dtype=input.dtype),
torch.as_tensor(params["angle"], device=input.device, dtype=input.dtype),
deg2rad(torch.as_tensor(params["sx"], device=input.device, dtype=input.dtype)),
deg2rad(torch.as_tensor(params["sy"], device=input.device, dtype=input.dtype)),
)
def apply_transform(
self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None
) -> Tensor:
_, _, height, width = input.shape
transform = cast(Tensor, transform)
return warp_affine(
input,
transform[:, :2, :],
(height, width),
flags["resample"].name.lower(),
align_corners=flags["align_corners"],
padding_mode=flags["padding_mode"].name.lower(),
)
def inverse_transform(
self,
input: Tensor,
flags: Dict[str, Any],
transform: Optional[Tensor] = None,
size: Optional[Tuple[int, int]] = None,
) -> Tensor:
return self.apply_transform(
input,
params=self._params,
transform=torch.as_tensor(transform, device=input.device, dtype=input.dtype),
flags=flags,
)