Defining trainers and evaluators

If I have a training step that takes a different form from the inferencing step, and I have defined these functions separately,

def train_step(e, b):
    ...
    ...
    return batch_loss
trainer = Engine(train_step)

and

def infer_step(e, b):
    ...
    return batch_loss
evaluator = Engine(infer_step)

do I still need to define create_supervised_trainer and create_supervised_evaluator? If yes, do I need to define

def run_traiin(engine):
    train_evaluator.run(train_loader)

def run_validation(engine):
    infer_evaluator.run(valid_loader) 

Do I need to do trainer.add_event_handler(Events.EPOCH_STARTED(every=1), run_train) evaluator.add_event_handler(Events.EPOCH_STARTED(every=1), run_validation)

I am a bit confused here.

Or I can do @trainer.on(Events...) add the evaluation execution here

[vfdev-5]

@etetteh you can reach out to me at discord: vfdev-5 #4458 or via the email (avoiding basics bots, to get the email, please, run git log -1 on the cloned repository).

[etetteh]

Thanks, and you’ve been of great help. Wish I could contact you for a discussion via email. I will check the profiling. My images are 224X224, and I guess that’s also a reason I installed from the repo.