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

from typing import Any, Dict, Optional, Tuple, Union, cast

import torch
from torch import Tensor

from kornia.augmentation import random_generator as rg
from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
from kornia.geometry.bbox import bbox_generator, bbox_to_mask

[docs]class RandomErasing(IntensityAugmentationBase2D): r"""Erase a random rectangle of a tensor image according to a probability p value. .. image:: _static/img/RandomErasing.png The operator removes image parts and fills them with zero values at a selected rectangle for each of the images in the batch. The rectangle will have an area equal to the original image area multiplied by a value uniformly sampled between the range [scale[0], scale[1]) and an aspect ratio sampled between [ratio[0], ratio[1]) Args: scale: range of proportion of erased area against input image. ratio: range of aspect ratio of erased area. value: the value to fill the erased area. same_on_batch: apply the same transformation across the batch. p: probability that the random erasing operation will be performed. 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: Input tensor must be float and normalized into [0, 1] for the best differentiability support. Additionally, this function accepts another transformation tensor (:math:`(B, 3, 3)`), then the applied transformation will be merged int to the input transformation tensor and returned. Examples: >>> rng = torch.manual_seed(0) >>> inputs = torch.ones(1, 1, 3, 3) >>> aug = RandomErasing((.4, .8), (.3, 1/.3), p=0.5) >>> aug(inputs) tensor([[[[1., 0., 0.], [1., 0., 0.], [1., 0., 0.]]]]) To apply the exact augmenation again, you may take the advantage of the previous parameter state: >>> input = torch.randn(1, 3, 32, 32) >>> aug = RandomErasing((.4, .8), (.3, 1/.3), p=1.) >>> (aug(input) == aug(input, params=aug._params)).all() tensor(True) """ # Note: Extra params, inplace=False in Torchvision. def __init__( self, scale: Union[Tensor, Tuple[float, float]] = (0.02, 0.33), ratio: Union[Tensor, Tuple[float, float]] = (0.3, 3.3), value: float = 0.0, 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, keepdim=keepdim) self.scale = scale self.ratio = ratio self.value: float = float(value) self._param_generator = cast(rg.RectangleEraseGenerator, rg.RectangleEraseGenerator(scale, ratio, float(value))) def apply_transform( self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None ) -> Tensor: _, c, h, w = input.size() values = params["values"].unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).repeat(1, *input.shape[1:]).to(input) bboxes = bbox_generator(params["xs"], params["ys"], params["widths"], params["heights"]) mask = bbox_to_mask(bboxes, w, h) # Returns B, H, W mask = mask.unsqueeze(1).repeat(1, c, 1, 1).to(input) # Transform to B, c, H, W transformed = torch.where(mask == 1.0, values, input) return transformed