I would try to lower the impact of the imbalance a bit and subsample the negative samples before training to e.g. 15000 samples (or even lower), and then retry the WeightedRandomSampler.
Btw. could you post the code snippet you are using to create the class counts, weights, and WeightedRandomSampler?
I would like to make sure nothing goes wrong there, as your model still overfits badly on the negative class.