ReduceLROnPlateau LRScheduler error

Hi,

I tried to use the LRScheduler handler for ReduceLROnPlateau from torch.optim and it threw an a TypeError:

plateau_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer, mode='min', factor=0.5, patience=1)
ignite_plateau_scheduler = LRScheduler(plateau_scheduler)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-13-56cb18e5af57> in <module>
      1 plateau_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer, mode='min', factor=0.5, patience=1)
----> 2 ignite_plateau_scheduler = LRScheduler(plateau_scheduler)

/net/vaosl01/opt/NFS/su0/anaconda3/envs/nlpbook/lib/python3.7/site-packages/pytorch_ignite-0.2.0-py3.7.egg/ignite/contrib/handlers/param_scheduler.py in __init__(self, lr_scheduler, save_history, **kwds)
    424         if not isinstance(lr_scheduler, _LRScheduler):
    425             raise TypeError("Argument lr_scheduler should be a subclass of torch.optim.lr_scheduler._LRScheduler, "
--> 426                             "but given {}".format(type(lr_scheduler)))
    427 
    428         if len(lr_scheduler.optimizer.param_groups) > 1:

TypeError: Argument lr_scheduler should be a subclass of torch.optim.lr_scheduler._LRScheduler, but given <class 'torch.optim.lr_scheduler.ReduceLROnPlateau'>

The example from the documentation with StepLR works without problems. It seems that the objects belong to different base classes. This is indeed true as can be from the source here. StepLR inherits from _LRScheduler, while ReduceLROnPlateau does not have a base class (inherits from object). Is there a way to bypass that check for this scheduler?

Thanks.

[ismael-elatifi]

I wrote a wrapper of torch.optim.lr_scheduler.ReduceLROnPlateau and put it here as it might be useful to others.

from torch.optim.lr_scheduler import ReduceLROnPlateau


class ReduceLROnPlateauScheduler:
    """Wrapper of torch.optim.lr_scheduler.ReduceLROnPlateau with __call__ method like other schedulers in 
        contrib\handlers\param_scheduler.py"""

    def __init__(
            self,
            optimizer,
            metric_name,
            mode='min', factor=0.1, patience=10,
            threshold=1e-4, threshold_mode='rel', cooldown=0,
            min_lr=0, eps=1e-8, verbose=False,
    ):
        self.metric_name = metric_name
        self.scheduler = ReduceLROnPlateau(optimizer, mode=mode, factor=factor, patience=patience,
                                           threshold=threshold, threshold_mode=threshold_mode, cooldown=cooldown,
                                           min_lr=min_lr, eps=eps, verbose=verbose)

    def __call__(self, engine, name=None):
        self.scheduler.step(engine.state.metrics[self.metric_name])

    def state_dict(self):
        return self.scheduler.state_dict()

# usage example
optimizer = torch.optim.Adam(model.parameters())
scheduler = ReduceLROnPlateauScheduler(optimizer, metric_name="my_metric", mode="max")
evaluator.add_event_handler(Events.COMPLETED, scheduler)

[sudarshan85]

Ok thanks. I just started using ignite today. Thats why I’m having some basic questions. Thanks for your help.