code: def __init__(
      self,
      data_augmentation,
      hflip_prob=0.5,
      mean=(123.0, 117.0, 104.0),
  ):
      if data_augmentation == "hflip":
          self.transforms = T.Compose(
              [
                  T.RandomHorizontalFlip(p=hflip_prob),
                  T.PILToTensor(),
                  T.ConvertImageDtype(torch.float),
              ]
          )
      elif data_augmentation == "ssd":
          self.transforms = T.Compose(
              [
                  T.RandomPhotometricDistort(),
                  T.RandomZoomOut(fill=list(mean)),
                  T.RandomIoUCrop(),
                  T.RandomHorizontalFlip(p=hflip_prob),
                  T.PILToTensor(),
                  T.ConvertImageDtype(torch.float),
              ]
          )
      elif data_augmentation == "ssdlite":
          self.transforms = T.Compose(
              [
                  T.RandomIoUCrop(),
                  T.RandomHorizontalFlip(p=hflip_prob),
                  T.PILToTensor(),
                  T.ConvertImageDtype(torch.float),
              ]
          )
      else:
          raise ValueError(
              f'Unknown data augmentation policy "{data_augmentation}"'
          )
  
file_path: avalanche/examples/tvdetection/presets.py
imports: import torch
  import transforms
label_desc: Use image node_id 1 to create a new DetectionPresetTrain object  with example: obj = DetectionPresetTrain(data_augmentation, hflip_prob, mean)