Hello, I am training a binary classification model and I want my results to be reproducible. So, I am using this function before every training session begins:

def seed_everything(seed_value, use_cuda):
if use_cuda:
torch.cuda.manual_seed(seed_value)
torch.cuda.manual_seed_all(seed_value)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False

What I am asking is, should I seed_everything even if I loaded a model using last model.state_dict() from a previous session?
I am asking this because for example if I trained for 30 epochs and saved model weights at epoch 20, then I start from epoch 20 again, the results of epoch 20 to 30 are not the same as the last training session.