class_docstr: Transforms images for VICRegL.
  
  Input to this transform:
      PIL Image or Tensor.
  
  Output of this transform:
      List of Tensor of length n_global_views + n_local_views. (8 by default)
  
  Applies the following augmentations by default:
      - Random resized crop
      - Random horizontal flip
      - Color jitter
      - Random gray scale
      - Gaussian blur
      - Random solarization
      - ImageNet normalization
  
  - [0]: VICRegL, 2022, https://arxiv.org/abs/2210.01571
  
  Attributes:
      global_crop_size:
          Size of the input image in pixels for the global crop views.
      local_crop_size:
          Size of the input image in pixels for the local crop views.
      n_global_views:
          Number of global crop views to generate.
      n_local_views:
          Number of local crop views to generate. For ResNet backbones it is
          recommended to set this to 0, see [0].
      global_crop_scale:
          Min and max scales for the global crop views.
      local_crop_scale:
          Min and max scales for the local crop views.
      global_grid_size:
          Grid size for the global crop views.
      local_grid_size:
          Grid size for the local crop views.
      global_gaussian_blur_prob:
          Probability of Gaussian blur for the global crop views.
      local_gaussian_blur_prob:
          Probability of Gaussian blur for the local crop views.
      global_gaussian_blur_kernel_size:
          Will be deprecated in favor of `global_gaussian_blur_sigmas` argument.
          If set, the old behavior applies and `global_gaussian_blur_sigmas`
          is ignored. Used to calculate sigma of gaussian blur with
          global_gaussian_blur_kernel_size * input_size. Applied to global crop views.
      local_gaussian_blur_kernel_size:
          Will be deprecated in favor of `local_gaussian_blur_sigmas` argument.
          If set, the old behavior applies and `local_gaussian_blur_sigmas`
          is ignored. Used to calculate sigma of gaussian blur with
          local_gaussian_blur_kernel_size * input_size. Applied to local crop views.
      global_gaussian_blur_sigmas:
          Tuple of min and max value from which the std of the gaussian kernel
          is sampled. It is ignored if `global_gaussian_blur_kernel_size` is set.
          Applied to global crop views.
      local_gaussian_blur_sigmas:
          Tuple of min and max value from which the std of the gaussian kernel
          is sampled. It is ignored if `local_gaussian_blur_kernel_size` is set.
          Applied to local crop views.
      global_solarize_prob:
          Probability of solarization for the global crop views.
      local_solarize_prob:
          Probability of solarization for the local crop views.
      hf_prob:
          Probability that horizontal flip is applied.
      cj_prob:
          Probability that color jitter is applied.
      cj_strength:
          Strength of the color jitter. `cj_bright`, `cj_contrast`, `cj_sat`, and
          `cj_hue` are multiplied by this value.
      cj_bright:
          How much to jitter brightness.
      cj_contrast:
          How much to jitter constrast.
      cj_sat:
          How much to jitter saturation.
      cj_hue:
          How much to jitter hue.
      random_gray_scale:
          Probability of conversion to grayscale.
      normalize:
          Dictionary with mean and standard deviation for normalization.
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code: def __init__(
      self,
      global_crop_size: int = 224,
      local_crop_size: int = 96,
      n_global_views: int = 2,
      n_local_views: int = 6,
      global_crop_scale: Tuple[float, float] = (0.2, 1.0),
      local_crop_scale: Tuple[float, float] = (0.05, 0.2),
      global_grid_size: int = 7,
      local_grid_size: int = 3,
      global_gaussian_blur_prob: float = 0.5,
      local_gaussian_blur_prob: float = 0.1,
      global_gaussian_blur_kernel_size: Optional[float] = None,
      local_gaussian_blur_kernel_size: Optional[float] = None,
      global_gaussian_blur_sigmas: Tuple[float, float] = (0.1, 2),
      local_gaussian_blur_sigmas: Tuple[float, float] = (0.1, 2),
      global_solarize_prob: float = 0.0,
      local_solarize_prob: float = 0.2,
      hf_prob: float = 0.5,
      vf_prob: float = 0.0,
      cj_prob: float = 1.0,
      cj_strength: float = 0.5,
      cj_bright: float = 0.8,
      cj_contrast: float = 0.8,
      cj_sat: float = 0.4,
      cj_hue: float = 0.2,
      random_gray_scale: float = 0.2,
      normalize: Union[
          None, Dict[str, List[float]]
      ] = IMAGENET_NORMALIZE,
  ):
      global_transform = (
          RandomResizedCropAndFlip(
              crop_size=global_crop_size,
              crop_min_scale=global_crop_scale[0],
              crop_max_scale=global_crop_scale[1],
              hf_prob=hf_prob,
              vf_prob=vf_prob,
              grid_size=global_grid_size,
          ),
          VICRegLViewTransform(
              gaussian_blur_prob=global_gaussian_blur_prob,
              gaussian_blur_kernel_size=global_gaussian_blur_kernel_size,
              gaussian_blur_sigmas=global_gaussian_blur_sigmas,
              solarize_prob=global_solarize_prob,
              cj_prob=cj_prob,
              cj_strength=cj_strength,
              cj_bright=cj_bright,
              cj_contrast=cj_contrast,
              cj_sat=cj_sat,
              cj_hue=cj_hue,
              random_gray_scale=random_gray_scale,
              normalize=normalize,
          ),
      )
      local_transform = (
          RandomResizedCropAndFlip(
              crop_size=local_crop_size,
              crop_min_scale=local_crop_scale[0],
              crop_max_scale=local_crop_scale[1],
              hf_prob=hf_prob,
              grid_size=local_grid_size,
          ),
          VICRegLViewTransform(
              gaussian_blur_prob=local_gaussian_blur_prob,
              gaussian_blur_kernel_size=local_gaussian_blur_kernel_size,
              gaussian_blur_sigmas=local_gaussian_blur_sigmas,
              solarize_prob=local_solarize_prob,
              cj_prob=cj_prob,
              cj_strength=cj_strength,
              random_gray_scale=random_gray_scale,
              normalize=normalize,
          ),
      )
  
      transforms = [global_transform] * n_global_views + [
          local_transform
      ] * n_local_views
      super().__init__(transforms=transforms)
  
file_path: lightly/lightly/transforms/vicregl_transform.py
imports: from typing import Dict, List, Optional, Tuple, Union
  import torchvision.transforms
  from PIL.Image import Image
  from torch import Tensor
  from lightly.transforms.gaussian_blur import GaussianBlur
  from lightly.transforms.image_grid_transform import ImageGridTransform
  from lightly.transforms.random_crop_and_flip_with_grid import RandomResizedCropAndFlip
  from lightly.transforms.solarize import RandomSolarization
  from lightly.transforms.utils import IMAGENET_NORMALIZE
label_desc: Use image node_id 1 to create a new VICRegLTransform object from inherited base classes: ImageGridTransform with example: obj = VICRegLTransform(global_crop_size, local_crop_size, n_global_views, n_local_views, global_crop_scale, local_crop_scale, global_grid_size, local_grid_size, global_gaussian_blur_prob, local_gaussian_blur_prob, global_gaussian_blur_kernel_size, local_gaussian_blur_kernel_size, global_gaussian_blur_sigmas, local_gaussian_blur_sigmas, global_solarize_prob, local_solarize_prob, hf_prob, vf_prob, cj_prob, cj_strength, cj_bright, cj_contrast, cj_sat, cj_hue, random_gray_scale, normalize)