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

from typing import Any, Dict, Optional, Union

import torch
from torch import Tensor

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


[docs]class RandomInvert(IntensityAugmentationBase2D): r"""Invert the tensor images values randomly. .. image:: _static/img/RandomInvert.png Args: max_val: The expected maximum value in the input tensor. The shape has to according to the input tensor shape, or at least has to work with broadcasting. 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). .. note:: This function internally uses :func:`kornia.enhance.invert`. Examples: >>> rng = torch.manual_seed(0) >>> img = torch.rand(1, 1, 5, 5) >>> inv = RandomInvert() >>> inv(img) tensor([[[[0.4963, 0.7682, 0.0885, 0.1320, 0.3074], [0.6341, 0.4901, 0.8964, 0.4556, 0.6323], [0.3489, 0.4017, 0.0223, 0.1689, 0.2939], [0.5185, 0.6977, 0.8000, 0.1610, 0.2823], [0.6816, 0.9152, 0.3971, 0.8742, 0.4194]]]]) To apply the exact augmenation again, you may take the advantage of the previous parameter state: >>> input = torch.randn(1, 3, 32, 32) >>> aug = RandomInvert(p=1.) >>> (aug(input) == aug(input, params=aug._params)).all() tensor(True) """ def __init__( self, max_val: Union[float, Tensor] = torch.tensor(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(max_val=max_val) def apply_transform( self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None ) -> Tensor: return invert(input, torch.as_tensor(flags["max_val"], device=input.device, dtype=input.dtype))