RandAugment implementation differences.

I’m reading keras_cv RandAugment implementation and feel like there are some differences with TF implementation referenced in the paper. I’m still reading this layer so I might not have understood everything, but here it goes:

• Missing Posterization op in RandAugment - reference TF Implementation.
• Missing Sharpness op in RandAugment - this op is present as a Random Layer in keras-cv.
• Missing Rotate op in RandAugment - discussed also in #402 .
• Missing random magnitude negation in shear and translate ops - reference TF implementation randomly negates magnitudes for these ops, and it looks like it’s also used in RandAugment.
• Different behaviour for Random* layers, e.g RandomBrightness is used instead of Brightness, but the function behaves differently in keras and differently in TF implementation. Perhaps this is marginal.

Are those differences intentional / are the missing operations not so important or should they be added in the future?

[LukeWood]

I actually discussed this heavily with Ekin and am confident that our implementation is correct and that the others are actually NOT correct!

I can take an action item to discuss this in a “why KerasCV doc”; I am actively working with several original authors to verify our implementations of various components.

[sebastian-sz]

@bhack If those differences were discussed and accepted maybe they could be described in the docstring.

For example: the Identity operation is also missing but it’s mentioned that this is substituted by the rate parameter.