Decays the learning rate of each parameter group by linearly changing small multiplicative factor until the number of epoch reaches a pre-defined milestone: end.
Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.
optimizer (Optimizer or OptimWrapper) – Wrapped optimizer.
start_factor (float) – The number we multiply learning rate in the first epoch. The multiplication factor changes towards end_factor in the following epochs. Defaults to 1./3.
end_factor (float) – The number we multiply learning rate at the end of linear changing process. Defaults to 1.0.
begin (int) – Step at which to start updating the learning rate. Defaults to 0.
end (int) – Step at which to stop updating the learning rate. Defaults to INF.
last_step (int) – The index of last step. Used for resume without state dict. Defaults to -1.
by_epoch (bool) – Whether the scheduled learning rate is updated by epochs. Defaults to True.
verbose (bool) – Whether to print the learning rate for each update. Defaults to False.
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