I don’t think that we can do this claim in general as the two profiling methods that I’ve linked are always the profiling GT for your specific model and augmentation needs (as It could depend on model size, input size, augmentation complexity etc…).
You can check how was organized some recent STOA augmentations in:

  

      github.com/keras-team/keras-cv
  

  
    
  
    
  

  
    
      Add MixUp & CutMix preprocessing layers 1
    

    
      keras-team:master ← keras-team:makecommit
    

    
      
        opened 
        
          
        
        Dec 29, 2021
      
      

      
        
          
          LukeWood
        
      

      
        
          +532
          -1
        
      
    
  


  
    Some example images generated using the MixUp preprocessing layer:

![https://…imgur.com/F9tue8L.png](https://imgur.com/F9tue8L.png)

CutMix:

![https://i.imgur.com/3W0ulwj.png](https://i.imgur.com/3W0ulwj.png)

fixes #19
  

  

  
    
    
  

  


Check also my comment and the related reply at:

  

      github.com/keras-team/keras-cv
  

  
    
  
    
  

  
    
      Add MixUp & CutMix preprocessing layers
    

    
      keras-team:master ← keras-team:makecommit
    

    
      
        opened 
        
          
        
        Dec 29, 2021
      
      

      
        
          
          LukeWood
        
      

      
        
          +532
          -1
        
      
    
  


  
    Some example images generated using the MixUp preprocessing layer:

![https://…imgur.com/F9tue8L.png](https://imgur.com/F9tue8L.png)

CutMix:

![https://i.imgur.com/3W0ulwj.png](https://i.imgur.com/3W0ulwj.png)

fixes #19
  

  

  
    
    
  

  


/cc @Luke_Wood