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Is there something specific about numpy here, or would it be any RNG? I'm looking at some code that uses random.random() to randomly apply augmentations, I sus
by andrew_v4 6y ago
Is there something specific about numpy here, or would it be any RNG?
I'm looking at some code that uses random.random() to randomly apply augmentations, I suspect that will have the same issue right?
- rurban 6y agoIt is a well-known trap that forked or threaded RNG's keep using the same seed. It can be called a feature, eg when you need to process different ranges, using the same sequence of random numbers for each range, but mostly it's a bug. You need to init your rng seed for each thread or fork to get different random sequences. For splitting up sequential ranges, a good rng typically has an advance function to advance the seed for each range. So you can get reproducibility.