I am aware of the preprocessing proto that is used in the models repo:

  

      github.com
  

  
    tensorflow/models/blob/master/research/object_detection/protos/preprocessor.proto 3


    syntax = "proto2";

package object_detection.protos;

// Message for defining a preprocessing operation on input data.
// See: //third_party/tensorflow_models/object_detection/core/preprocessor.py
// Next ID: 41
message PreprocessingStep {
  oneof preprocessing_step {
    NormalizeImage normalize_image = 1;
    RandomHorizontalFlip random_horizontal_flip = 2;
    RandomPixelValueScale random_pixel_value_scale = 3;
    RandomImageScale random_image_scale = 4;
    RandomRGBtoGray random_rgb_to_gray = 5;
    RandomAdjustBrightness random_adjust_brightness = 6;
    RandomAdjustContrast random_adjust_contrast = 7;
    RandomAdjustHue random_adjust_hue = 8;
    RandomAdjustSaturation random_adjust_saturation = 9;
    RandomDistortColor random_distort_color = 10;
    RandomJitterBoxes random_jitter_boxes = 11;



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My question is how one configures their augmentation pipeline when using the TFOD API. Consider this configuration file. It has a field for augmentation:
train_config: {
  ...
  data_augmentation_options {
    random_horizontal_flip {
    }
  }

If I wanted to expand the set of augmentation transformations here what should I do?
@Laurence_Moroney @khanhlvg any pointers?