Currently am fixing the all seeds by doing the following,
random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False

I want to train multiple models of the same architecture type but with different initialization and ensemble them later.
I usually change the seed to get a different init but since am fixing the seed for everything, how do I still get a different init?