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


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, 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]: 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