from typing import Any, Dict, Optional, Tuple
import torch
from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
from kornia.core import Tensor
def _randn_like(input: Tensor, mean: float, std: float) -> Tensor:
x = torch.randn_like(input) # Generating on GPU is fastest with `torch.randn_like(...)`
if std != 1.0: # `if` is cheaper than multiplication
x *= std
if mean != 0.0: # `if` is cheaper than addition
x += mean
return x
[docs]class RandomGaussianNoise(IntensityAugmentationBase2D):
r"""Add gaussian noise to a batch of multi-dimensional images.
.. image:: _static/img/RandomGaussianNoise.png
Args:
mean: The mean of the gaussian distribution.
std: The standard deviation of the gaussian distribution.
same_on_batch: apply the same transformation across the batch.
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).
Examples:
>>> rng = torch.manual_seed(0)
>>> img = torch.ones(1, 1, 2, 2)
>>> RandomGaussianNoise(mean=0., std=1., p=1.)(img)
tensor([[[[ 2.5410, 0.7066],
[-1.1788, 1.5684]]]])
To apply the exact augmenation again, you may take the advantage of the previous parameter state:
>>> input = torch.randn(1, 3, 32, 32)
>>> aug = RandomGaussianNoise(mean=0., std=1., p=1.)
>>> (aug(input) == aug(input, params=aug._params)).all()
tensor(True)
"""
def __init__(
self, mean: float = 0.0, std: float = 1.0, same_on_batch: bool = False, p: float = 0.5, keepdim: bool = False
) -> None:
super().__init__(p=p, same_on_batch=same_on_batch, p_batch=1.0, keepdim=keepdim)
self.flags = {"mean": mean, "std": std}
def generate_parameters(self, shape: Tuple[int, ...]) -> Dict[str, Tensor]:
return {}
def apply_transform(
self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None
) -> Tensor:
if "gaussian_noise" in params:
gaussian_noise = params["gaussian_noise"]
else:
gaussian_noise = _randn_like(input, mean=flags["mean"], std=flags["std"])
self._params["gaussian_noise"] = gaussian_noise
return input + gaussian_noise