# Source code for kornia.augmentation._2d.intensity.gaussian_blur

```from typing import Any, Dict, Optional, Tuple

from torch import Tensor

from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
from kornia.constants import BorderType
from kornia.filters import gaussian_blur2d

[docs]class RandomGaussianBlur(IntensityAugmentationBase2D):
r"""Apply gaussian blur given tensor image or a batch of tensor images randomly.

.. image:: _static/img/RandomGaussianBlur.png

Args:
kernel_size: the size of the kernel.
sigma: the standard deviation of the kernel.
border_type: the padding mode to be applied before convolving.
The expected modes are: ``constant``, ``reflect``, ``replicate`` or ``circular``.
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).

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.filters.gaussian_blur2d`.

Examples:
>>> rng = torch.manual_seed(0)
>>> input = torch.rand(1, 1, 5, 5)
>>> blur = RandomGaussianBlur((3, 3), (0.1, 2.0), p=1.)
>>> blur(input)
tensor([[[[0.6699, 0.4645, 0.3193, 0.1741, 0.1955],
[0.5422, 0.6657, 0.6261, 0.6527, 0.5195],
[0.3826, 0.2638, 0.1902, 0.1620, 0.2141],
[0.6329, 0.6732, 0.5634, 0.4037, 0.2049],
[0.8307, 0.6753, 0.7147, 0.5768, 0.7097]]]])

To apply the exact augmenation again, you may take the advantage of the previous parameter state:
>>> input = torch.randn(1, 3, 32, 32)
>>> aug = RandomGaussianBlur((3, 3), (0.1, 2.0), p=1.)
>>> (aug(input) == aug(input, params=aug._params)).all()
tensor(True)
"""

def __init__(
self,
kernel_size: Tuple[int, int],
sigma: Tuple[float, float],
border_type: str = "reflect",
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(kernel_size=kernel_size, sigma=sigma, border_type=BorderType.get(border_type))

def apply_transform(
self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None
) -> Tensor:
return gaussian_blur2d(input, flags["kernel_size"], flags["sigma"], flags["border_type"].name.lower())
```