ChainedScheduler
class torch.optim.lr_scheduler.ChainedScheduler(schedulers)
Chains list of learning rate schedulers. It takes a list of
   chainable learning rate schedulers and performs consecutive step()
   functions belonging to them by just one call.
Parameters:
      schedulers (list) -- List of chained schedulers.
-[ Example ]-



Assuming optimizer uses lr = 1. for all groups
lr = 0.09     if epoch == 0
lr = 0.081    if epoch == 1
lr = 0.729    if epoch == 2
lr = 0.6561   if epoch == 3
lr = 0.59049  if epoch >= 4
scheduler1 = ConstantLR(self.opt, factor=0.1, total_iters=2)
scheduler2 = ExponentialLR(self.opt, gamma=0.9)
scheduler = ChainedScheduler([scheduler1, scheduler2])
for epoch in range(100):
    train(...)
    validate(...)
    scheduler.step()



get_last_lr()
  Return last computed learning rate by current scheduler.

load_state_dict(state_dict)