Interesting.
This looks like they’ve re-encoded something like keras’s model.get_config, as a proto.
To change the data-augmentation, you edit that data_augmentation_options list.
The .proto files define what’s allowed. The definition of TrainConfig is here:

  

      github.com
  

  
    tensorflow/models/blob/aa3e639f80c2967504310b0f578f0f00063a8aff/research/object_detection/protos/train.proto#L25



      
      
          
          
// Message for configuring DetectionModel training jobs (train.py).
          // Next id: 31
          message TrainConfig {
            // Effective batch size to use for training.
            // For TPU (or sync SGD jobs), the batch size per core (or GPU) is going to be
            // `batch_size` / number of cores (or `batch_size` / number of GPUs).
            optional uint32 batch_size = 1 [default=32];
          
          
  // Data augmentation options.
            repeated PreprocessingStep data_augmentation_options = 2;
          
          
  // Whether to synchronize replicas during training.
            optional bool sync_replicas = 3 [default=false];
          
          
  // How frequently to keep checkpoints.
            optional float keep_checkpoint_every_n_hours = 4 [default=10000.0];
          
          
  // Optimizer used to train the DetectionModel.
            optional Optimizer optimizer = 5;
          
      

    
  


  

  
    
    
  

  


data_augmentation_options is a repeated PreprocessingStep.
A PreprocessingStep is one of the items from that list. The parameters of each and their default values are defined in preprocessor.proto
If you want to add a RandomScale step:
train_config: {
  ...
  data_augmentation_options {
    random_horizontal_flip {
    }
   random_image_scale {
       min_scale_ratio: 0.9
       max_scale_ratio: 1.1
    }
  }
}

That format is “proto-text” (.PBTXT), you can check your syntax with:
from google.protobuf import text_format

train_config = TrainConfig()
train_config = text_format.Parse(
        r"""
        train_config: {
          ...
          data_augmentation_options {
            random_horizontal_flip {
            }
           random_image_scale {
               min_scale_ratio: 0.9
               max_scale_ratio: 1.1
            }
          }
        }
        """, train_config)
print(train_config)