I would like to compare offline and online data augmentation (DA). In terms of time and memory usage during training.
- Offline DA happens when the augmentation is done before training.
- Online DA happens in the data class and is performed for each mini-batch.
So I am looking in how to memory profile the dataloader in pytorch with and without DA.
Then I would like to profile the memory usage of the model during training.
I think DA happens on the CPU and the model is trained on the GPU.
How would you do that ?