Closed Dale-Black closed 3 years ago
So you want to fix the randomness of a stochastic transformation so it always gives the same result? What would seed
do exactly?
I was thinking something like this. But I’m not sure if this is much cleaner than the route you have already proposed actually
Hm, I don't really see an advantage in this. One thing to do could be to implement a wrapper transform Deterministic
that wraps a transform and always passes the same random state through. That way you wouldn't need to change existing transforms.
Fair enough. I think I agree with you the more I think about it
I would like to suggest an argument for
RandomResizeCrop
and other stochastic transformations. Something likeRandomResizeCrop(seed=...)
would be really convenient for augmenting supervised data with the same random seed. This isn't necessary obviously as you have already given me a helpful workaround, but in case this is easy to implement I wanted to add this suggestion