anomalib: [Bug]: Training EfficientAd failed: ZeroDivisionError: integer division or modulo by zero

Describe the bug

Hi, I have tried to run the following example in last anomalib version:

from anomalib.data import MVTec
from anomalib.models import EfficientAd
from anomalib.engine import Engine

from anomalib.callbacks import ModelCheckpoint
from lightning.pytorch.callbacks import EarlyStopping

datamodule = MVTec()
model = EfficientAd()
engine = Engine()

engine.fit(datamodule=datamodule, model=model)

But It gives the following error:

ZeroDivisionError                         Traceback (most recent call last)

[<ipython-input-7-4052201ff8ac>](https://localhost:8080/#) in <cell line: 3>()
      1 # start training
      2 engine = Engine(task=TaskType.SEGMENTATION)
----> 3 engine.fit(model=model, datamodule=datamodule)

15 frames

[/content/anomalib/src/anomalib/engine/engine.py](https://localhost:8080/#) in fit(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
    354             self.trainer.validate(model, val_dataloaders, datamodule=datamodule, ckpt_path=ckpt_path)
    355         else:
--> 356             self.trainer.fit(model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
    357 
    358     def validate(

[/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/trainer.py](https://localhost:8080/#) in fit(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
    542         self.state.status = TrainerStatus.RUNNING
    543         self.training = True
--> 544         call._call_and_handle_interrupt(
    545             self, self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path
    546         )

[/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/call.py](https://localhost:8080/#) in _call_and_handle_interrupt(trainer, trainer_fn, *args, **kwargs)
     42         if trainer.strategy.launcher is not None:
     43             return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
---> 44         return trainer_fn(*args, **kwargs)
     45 
     46     except _TunerExitException:

[/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/trainer.py](https://localhost:8080/#) in _fit_impl(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
    578             model_connected=self.lightning_module is not None,
    579         )
--> 580         self._run(model, ckpt_path=ckpt_path)
    581 
    582         assert self.state.stopped

[/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/trainer.py](https://localhost:8080/#) in _run(self, model, ckpt_path)
    987         # RUN THE TRAINER
    988         # ----------------------------
--> 989         results = self._run_stage()
    990 
    991         # ----------------------------

[/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/trainer.py](https://localhost:8080/#) in _run_stage(self)
   1033                 self._run_sanity_check()
   1034             with torch.autograd.set_detect_anomaly(self._detect_anomaly):
-> 1035                 self.fit_loop.run()
   1036             return None
   1037         raise RuntimeError(f"Unexpected state {self.state}")

[/usr/local/lib/python3.10/dist-packages/lightning/pytorch/loops/fit_loop.py](https://localhost:8080/#) in run(self)
    200             try:
    201                 self.on_advance_start()
--> 202                 self.advance()
    203                 self.on_advance_end()
    204                 self._restarting = False

[/usr/local/lib/python3.10/dist-packages/lightning/pytorch/loops/fit_loop.py](https://localhost:8080/#) in advance(self)
    357         with self.trainer.profiler.profile("run_training_epoch"):
    358             assert self._data_fetcher is not None
--> 359             self.epoch_loop.run(self._data_fetcher)
    360 
    361     def on_advance_end(self) -> None:

[/usr/local/lib/python3.10/dist-packages/lightning/pytorch/loops/training_epoch_loop.py](https://localhost:8080/#) in run(self, data_fetcher)
    134         while not self.done:
    135             try:
--> 136                 self.advance(data_fetcher)
    137                 self.on_advance_end(data_fetcher)
    138                 self._restarting = False

[/usr/local/lib/python3.10/dist-packages/lightning/pytorch/loops/training_epoch_loop.py](https://localhost:8080/#) in advance(self, data_fetcher)
    248         self.update_lr_schedulers("step", update_plateau_schedulers=False)
    249         if self._num_ready_batches_reached():
--> 250             self.update_lr_schedulers("epoch", update_plateau_schedulers=False)
    251 
    252         if using_dataloader_iter:

[/usr/local/lib/python3.10/dist-packages/lightning/pytorch/loops/training_epoch_loop.py](https://localhost:8080/#) in update_lr_schedulers(self, interval, update_plateau_schedulers)
    335         if interval == "step" and self._should_accumulate():
    336             return
--> 337         self._update_learning_rates(interval=interval, update_plateau_schedulers=update_plateau_schedulers)
    338 
    339     def _update_learning_rates(self, interval: str, update_plateau_schedulers: bool) -> None:

[/usr/local/lib/python3.10/dist-packages/lightning/pytorch/loops/training_epoch_loop.py](https://localhost:8080/#) in _update_learning_rates(self, interval, update_plateau_schedulers)
    386 
    387                 # update LR
--> 388                 call._call_lightning_module_hook(
    389                     trainer,
    390                     "lr_scheduler_step",

[/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/call.py](https://localhost:8080/#) in _call_lightning_module_hook(trainer, hook_name, pl_module, *args, **kwargs)
    155 
    156     with trainer.profiler.profile(f"[LightningModule]{pl_module.__class__.__name__}.{hook_name}"):
--> 157         output = fn(*args, **kwargs)
    158 
    159     # restore current_fx when nested context

[/usr/local/lib/python3.10/dist-packages/lightning/pytorch/core/module.py](https://localhost:8080/#) in lr_scheduler_step(self, scheduler, metric)
   1246         """
   1247         if metric is None:
-> 1248             scheduler.step()  # type: ignore[call-arg]
   1249         else:
   1250             scheduler.step(metric)

[/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py](https://localhost:8080/#) in step(self, epoch)
    145             if epoch is None:
    146                 self.last_epoch += 1
--> 147                 values = self.get_lr()
    148             else:
    149                 warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)

[/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py](https://localhost:8080/#) in get_lr(self)
    385                           "please use `get_last_lr()`.", UserWarning)
    386 
--> 387         if (self.last_epoch == 0) or (self.last_epoch % self.step_size != 0):
    388             return [group['lr'] for group in self.optimizer.param_groups]
    389         return [group['lr'] * self.gamma

ZeroDivisionError: integer division or modulo by zero

Dataset

MVTec

Model

Other (please specify in the field below)

Steps to reproduce the behavior

Model: EfficientAd

OS information

OS information:

• Google Colab

Expected behavior

Train the model successfully.

Screenshots

No response

Pip/GitHub

pip

What version/branch did you use?

Branch: main anomalib version: v1

Configuration YAML

Default

Logs

Default

Code of Conduct

• I agree to follow this project’s Code of Conduct