Training with new Random Seed does not shuffle data

[keeganq]

I've been adapting the example scripts to my own training task, and I've noticed that the scripts do not handle different random seeds as expected. I've found this problem in two places, but there might be more:

virtex/scripts/clf_linear.py

Lines 104 to 109 in 2baba8a

	sampler=DistributedSampler(
	train_dataset,
	num_replicas=dist.get_world_size(),
	rank=dist.get_rank(),
	shuffle=True,
	),

virtex/scripts/pretrain_virtex.py

Line 68 in 2baba8a

DistributedSampler(train_dataset, shuffle=True) # type: ignore

The problem is that the DistributedSampler (from PyTorch 1.9.0) requires kwarg "seed" to shuffle differently, when shuffle=True. I believe that the correct use of DistributedSampler for training with different random seeds would be to add the kwarg seed=_DOWNC.RANDOM_SEED when DistributedSampler is initialized in these two places. As for reshuffling on additional epochs, DistributedSampler will add the seed to the epoch number, so nothing needs to be changed during epoch-setting for the sampler.

https://github.com/pytorch/pytorch/blob/d69c22dd61a2f006dcfe1e3ea8468a3ecaf931aa/torch/utils/data/distributed.py#L100

Please let me know your thoughts, or if I may have missed something.