My understanding was that transformations specified in batch_tfms apply to the train dataset only, leaving the valid dataset alone. However, after I wrote and a custom transformation and used it in batch_tfms, I find that it affects both the train and the valid datasets. This behavior is similar to that in v2. However, in v2 there existed a way to reset the valid dataset after the fact, but I can’t find any way of doing this in V3.
The class that call the custom transformation is this:
The DataBlock and train dataset show_batch is this:
The valid dataset show_batch is this:
As you can see. The valid dataset contains all the transformations, including rotations, and this destroys the training/validation process.
Can someone tell me how to reset the valid dataset to it’s pristine form?
