Source code for kornia.augmentation._2d.intensity.gaussian_noise

from typing import Any, Dict, Optional

import torch
from torch import Tensor

from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D

[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, return_transform: Optional[bool] = None, ) -> None: super().__init__( p=p, return_transform=return_transform, same_on_batch=same_on_batch, p_batch=1.0, keepdim=keepdim ) self.flags = dict(mean=mean, std=std) def generate_parameters(self, shape: torch.Size) -> Dict[str, Tensor]: noise = torch.randn(shape) return dict(noise=noise) def apply_transform( self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None ) -> Tensor: return input + params["noise"].to(input.device) * flags["std"] + flags["mean"]