GaussianNoise

class torchvision.transforms.v2.GaussianNoise(mean: float = 0.0, sigma: float = 0.1, clip=True)

Add gaussian noise to images or videos.

The input tensor is expected to be in […, 1 or 3, H, W] format, where … means it can have an arbitrary number of leading dimensions. Each image or frame in a batch will be transformed independently i.e. the noise added to each image will be different.

The input tensor is also expected to be of float dtype in [0, 1], or of uint8 dtype in [0, 255]. This transform does not support PIL images.

Regardless of the dtype used, the parameters of the function use the same scale, so a mean parameter of 0.5 will result in an average value increase of 0.5 units for float images, and an average increase of 127.5 units for uint8 images.

Parameters:

• mean (float) – Mean of the sampled normal distribution. Default is 0.

• sigma (float) – Standard deviation of the sampled normal distribution. Default is 0.1.

• clip (bool, optional) – Whether to clip the values after adding noise, be it to [0, 1] for floats or to [0, 255] for uint8. Setting this parameter to False may cause unsigned integer overflows with uint8 inputs. Default is True.

transform(inpt: Any, params: dict[str, Any]) → Any

Method to override for custom transforms.

See How to write your own v2 transforms