Hi,
I am currently fixing the seed values globally in my script using the following snippet:
seed_value=123457
np.random.seed(seed_value) # cpu vars
torch.manual_seed(seed_value) # cpu  vars
random.seed(seed_value) # Python
torch.cuda.manual_seed(seed_value)
torch.cuda.manual_seed_all(seed_value)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False

Further, I am using multiple processes in my DataLoader using num_workers > 0 to load frames for a video. Does the above snippet assign a fixed seed worker_id+seed_value  to every worker in an epoch?
I am also using random augmentations as a part of my data loading pipeline like RandomResizedCrop, RandomHorizontal Flipping from torchvision transforms. If the worker seed is fixed at  worker_id+seed_value , does that mean at each epoch, the data will go through the same set of augmentations?
If someone can clarify this, it would be of great help. Thanks!