_ParamScheduler — mmengine 0.10.7 documentation (original) (raw)
class mmengine.optim._ParamScheduler(optimizer, param_name, begin=0, end=1000000000, last_step=-1, by_epoch=True, verbose=False)[source]¶
Base class for parameter schedulers.
It should be inherited by all schedulers that schedule parameters in the optimizer’s param_groups. All subclasses should overwrite the_get_value() according to their own schedule strategy. The implementation is motivated byhttps://github.com/pytorch/pytorch/blob/master/torch/optim/lr_scheduler.py.
Parameters:
- optimizer (BaseOptimWrapper or Optimizer) – Wrapped optimizer.
- param_name (str) – Name of the parameter to be adjusted, such as
lr,momentum. - begin (int) – Step at which to start updating the parameters. Defaults to 0.
- end (int) – Step at which to stop updating the parameters. Defaults to INF.
- last_step (int) – The index of last step. Used for resuming without state dict. Default value
-1means thestepfunction is never be called before. Defaults to -1. - by_epoch (bool) – Whether the scheduled parameters are updated by epochs. Defaults to True.
- verbose (bool) – Whether to print the value for each update. Defaults to False.
Return the last computed value by current scheduler.
Returns:
A list of the last computed value of the optimizer’sparam_group.
Return type:
load_state_dict(state_dict)[source]¶
Loads the schedulers state.
Parameters:
state_dict (dict) – scheduler state. Should be an object returned from a call to state_dict().
print_value(is_verbose, group, value)[source]¶
Display the current parameter value.
Parameters:
- is_verbose (bool) – Whether to print the value.
- group (int) – The index of the current
param_group. - value (float) – The parameter value.
Returns the state of the scheduler as a dict.
It contains an entry for every variable in self.__dict__ which is not the optimizer.
Returns:
scheduler state.
Return type:
Adjusts the parameter value of each parameter group based on the specified schedule.