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But the problem doesn't occur when you use Python's random seeds, precisely because PyTorch sets the Python random seed (but not the Numpy one) in each worker: https://github.com/pytorch/pytorch/blob/98baad57642115d1f66723f6f10585ed933fd731/torch/utils/data/_utils/worker.py#L137, as mentioned in OP's post. So maybe the issue can be fixed by just setting the Numpy random seed like this in the Pytorch code base?
Use kornia.augmentation where this problem is solved doing the augmentations in batch outside the dataloader. https://github.com/kornia/kornia
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