Chains a list of learning rate schedulers.
Takes in a sequence of chainable learning rate schedulers and calls their step() functions consecutively in just one call to step().
>>> # Assuming optimizer uses lr = 0.05 for all groups >>> # lr = 0.05 if epoch == 0 >>> # lr = 0.0450 if epoch == 1 >>> # lr = 0.0405 if epoch == 2 >>> # ... >>> # lr = 0.00675 if epoch == 19 >>> # lr = 0.06078 if epoch == 20 >>> # lr = 0.05470 if epoch == 21 >>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=20) >>> scheduler2 = ExponentialLR(optimizer, gamma=0.9) >>> scheduler = ChainedScheduler([scheduler1, scheduler2], optimizer=optimizer) >>> for epoch in range(100): >>> train(...) >>> validate(...) >>> scheduler.step()
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