GaussianBlur — Torchvision 0.22 documentation (original) (raw)
class torchvision.transforms.v2.GaussianBlur(kernel_size: Union[int, Sequence[int]], sigma: Union[int, float, Sequence[float]] = (0.1, 2.0))[source]¶
Blurs image with randomly chosen Gaussian blur kernel.
The convolution will be using reflection padding corresponding to the kernel size, to maintain the input shape.
If the input is a Tensor, it is expected to have […, C, H, W] shape, where … means an arbitrary number of leading dimensions.
Parameters:
- kernel_size (int or sequence) – Size of the Gaussian kernel.
- sigma (float or tuple of python:float ( min , max )) – Standard deviation to be used for creating kernel to perform blurring. If float, sigma is fixed. If it is tuple of float (min, max), sigma is chosen uniformly at random to lie in the given range.
Examples using GaussianBlur:
static get_params(sigma_min: float, sigma_max: float) → float[source]¶
Choose sigma for random gaussian blurring.
Parameters:
- sigma_min (float) – Minimum standard deviation that can be chosen for blurring kernel.
- sigma_max (float) – Maximum standard deviation that can be chosen for blurring kernel.
Returns:
Standard deviation to be passed to calculate kernel for gaussian blurring.
Return type:
make_params(flat_inputs: List[Any]) → Dict[str, Any][source]¶
Method to override for custom transforms.
See How to write your own v2 transforms
transform(inpt: Any, params: Dict[str, Any]) → Any[source]¶
Method to override for custom transforms.