[question] How to override how `self.train_transform` is applied to batches in `LightningDataModule`.
❓ Questions and Help
What is your question?
I want to apply the same random transformation on both X and Y (both are batches if RGB image) of a training batch using Kornia after the batch has been moved to the GPU (or TPU).
I cannot figure out what part of PL I need to override to make this work.
As far as Kornia is concerned, I know how to get the state of a random transform and use it again for another batch. But I cannot understand where inside LightningDataModule I am supposed to write that code such that it works properly even for MultiGPU training.
Code
What have you tried?
I can simply include the augmentation step in the LightningModule's training step after receiving the batches, but that would decouple my LightningDataModule from it's transforms which I don't want to do.
Any help would be greatly appreciated !! 🙂
On another note, the documentation isn't very clear on how the self.train_transform property of LightningDataModule is applied to batches of data and how we can change that behavior (which is pretty much what my question is).
What's your environment?

OS: [Ubuntu 16.04 LTS]
Packaging [ conda ]
Version [0.8.5]