Pytorch Lightning error for 501a_training_a_model_with_cube .

[Dpat123]

i am getting following error while running 501a_training_a_model_with_cube example . after this . could you please let me know why i am getting this error .

trainer = Trainer(
    callbacks=callbacks,
    accelerator="auto",
    auto_scale_batch_size=False,
    check_val_every_n_epoch=1,
    devices=1,
    gpus=None,
    max_epochs=1,
    num_sanity_val_steps=0,
    val_check_interval=1.0,
)
trainer.fit(model=model, datamodule=datamodule)

{
	"name": "MisconfigurationException",
	"message": "Could not find the `LightningModule` attribute for the `torchmetrics.Metric` logged. You can fix this by calling `self.log(image_AUROC, ..., metric_attribute=name)` where `name` is one of ['image_threshold', 'pixel_threshold', 'image_metrics.AUROC', 'normalization_metrics']",
	"stack": "---------------------------------------------------------------------------
MisconfigurationException                 Traceback (most recent call last)
~\\AppData\\Local\\Temp\\ipykernel_26868\\842986886.py in <module>
     10     val_check_interval=1.0,
     11 )
---> 12 trainer.fit(model=model, datamodule=datamodule)

i:\\diamond\\DeepLearning\\venv\\Deeplearning\\anomalib_env\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py in fit(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
    607         self.strategy._lightning_module = model
    608         call._call_and_handle_interrupt(
--> 609             self, self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path
    610         )
    611 

i:\\diamond\\DeepLearning\\venv\\Deeplearning\\anomalib_env\\lib\\site-packages\\pytorch_lightning\\trainer\\call.py in _call_and_handle_interrupt(trainer, trainer_fn, *args, **kwargs)
     36             return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
     37         else:
---> 38             return trainer_fn(*args, **kwargs)
     39 
     40     except _TunerExitException:

i:\\diamond\\DeepLearning\\venv\\Deeplearning\\anomalib_env\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py in _fit_impl(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
    648             model_connected=self.lightning_module is not None,
    649         )
--> 650         self._run(model, ckpt_path=self.ckpt_path)
    651 
    652         assert self.state.stopped

i:\\diamond\\DeepLearning\\venv\\Deeplearning\\anomalib_env\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py in _run(self, model, ckpt_path)
   1110         self._checkpoint_connector.resume_end()
   1111 
-> 1112         results = self._run_stage()
   1113 
   1114         log.detail(f\"{self.__class__.__name__}: trainer tearing down\")

i:\\diamond\\DeepLearning\\venv\\Deeplearning\\anomalib_env\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py in _run_stage(self)
   1189         if self.predicting:
   1190             return self._run_predict()
-> 1191         self._run_train()
   1192 
   1193     def _pre_training_routine(self) -> None:

i:\\diamond\\DeepLearning\\venv\\Deeplearning\\anomalib_env\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py in _run_train(self)
   1212 
   1213         with torch.autograd.set_detect_anomaly(self._detect_anomaly):
-> 1214             self.fit_loop.run()
   1215 
   1216     def _run_evaluate(self) -> _EVALUATE_OUTPUT:

i:\\diamond\\DeepLearning\\venv\\Deeplearning\\anomalib_env\\lib\\site-packages\\pytorch_lightning\\loops\\loop.py in run(self, *args, **kwargs)
    197             try:
    198                 self.on_advance_start(*args, **kwargs)
--> 199                 self.advance(*args, **kwargs)
    200                 self.on_advance_end()
    201                 self._restarting = False

i:\\diamond\\DeepLearning\\venv\\Deeplearning\\anomalib_env\\lib\\site-packages\\pytorch_lightning\\loops\\fit_loop.py in advance(self)
    265         self._data_fetcher.setup(dataloader, batch_to_device=batch_to_device)
    266         with self.trainer.profiler.profile(\"run_training_epoch\"):
--> 267             self._outputs = self.epoch_loop.run(self._data_fetcher)
    268 
    269     def on_advance_end(self) -> None:

i:\\diamond\\DeepLearning\\venv\\Deeplearning\\anomalib_env\\lib\\site-packages\\pytorch_lightning\\loops\\loop.py in run(self, *args, **kwargs)
    198                 self.on_advance_start(*args, **kwargs)
    199                 self.advance(*args, **kwargs)
--> 200                 self.on_advance_end()
    201                 self._restarting = False
    202             except StopIteration:

i:\\diamond\\DeepLearning\\venv\\Deeplearning\\anomalib_env\\lib\\site-packages\\pytorch_lightning\\loops\\epoch\\training_epoch_loop.py in on_advance_end(self)
    248         if should_check_val:
    249             self.trainer.validating = True
--> 250             self._run_validation()
    251             self.trainer.training = True
    252 

i:\\diamond\\DeepLearning\\venv\\Deeplearning\\anomalib_env\\lib\\site-packages\\pytorch_lightning\\loops\\epoch\\training_epoch_loop.py in _run_validation(self)
    306 
    307         with torch.no_grad():
--> 308             self.val_loop.run()
    309 
    310     def _accumulated_batches_reached(self) -> bool:

i:\\diamond\\DeepLearning\\venv\\Deeplearning\\anomalib_env\\lib\\site-packages\\pytorch_lightning\\loops\\loop.py in run(self, *args, **kwargs)
    204         self._restarting = False
    205 
--> 206         output = self.on_run_end()
    207         return output
    208 

i:\\diamond\\DeepLearning\\venv\\Deeplearning\\anomalib_env\\lib\\site-packages\\pytorch_lightning\\loops\\dataloader\\evaluation_loop.py in on_run_end(self)
    178 
    179         # hook
--> 180         self._evaluation_epoch_end(self._outputs)
    181         self._outputs = []  # free memory
    182 

i:\\diamond\\DeepLearning\\venv\\Deeplearning\\anomalib_env\\lib\\site-packages\\pytorch_lightning\\loops\\dataloader\\evaluation_loop.py in _evaluation_epoch_end(self, outputs)
    286         # call the model epoch end
    287         hook_name = \"test_epoch_end\" if self.trainer.testing else \"validation_epoch_end\"
--> 288         self.trainer._call_lightning_module_hook(hook_name, output_or_outputs)
    289 
    290     def _on_evaluation_epoch_end(self) -> None:

i:\\diamond\\DeepLearning\\venv\\Deeplearning\\anomalib_env\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py in _call_lightning_module_hook(self, hook_name, pl_module, *args, **kwargs)
   1354 
   1355         with self.profiler.profile(f\"[LightningModule]{pl_module.__class__.__name__}.{hook_name}\"):
-> 1356             output = fn(*args, **kwargs)
   1357 
   1358         # restore current_fx when nested context

i:\\diamond\\DeepLearning\\venv\\Deeplearning\\anomalib_env\\lib\\site-packages\\anomalib\\models\\components\\base\\anomaly_module.py in validation_epoch_end(self, outputs)
    145             self._compute_adaptive_threshold(outputs)
    146         self._collect_outputs(self.image_metrics, self.pixel_metrics, outputs)
--> 147         self._log_metrics()
    148 
    149     def test_epoch_end(self, outputs: EPOCH_OUTPUT) -> None:

i:\\diamond\\DeepLearning\\venv\\Deeplearning\\anomalib_env\\lib\\site-packages\\anomalib\\models\\components\\base\\anomaly_module.py in _log_metrics(self)
    224             self.log_dict(self.image_metrics, prog_bar=False)
    225         else:
--> 226             self.log_dict(self.image_metrics, prog_bar=True)
    227 
    228     def _load_normalization_class(self, state_dict: OrderedDict[str, Tensor]) -> None:

i:\\diamond\\DeepLearning\\venv\\Deeplearning\\anomalib_env\\lib\\site-packages\\pytorch_lightning\\core\\module.py in log_dict(self, dictionary, prog_bar, logger, on_step, on_epoch, reduce_fx, enable_graph, sync_dist, sync_dist_group, add_dataloader_idx, batch_size, rank_zero_only)
    594                 add_dataloader_idx=add_dataloader_idx,
    595                 batch_size=batch_size,
--> 596                 rank_zero_only=rank_zero_only,
    597             )
    598 

i:\\diamond\\DeepLearning\\venv\\Deeplearning\\anomalib_env\\lib\\site-packages\\pytorch_lightning\\core\\module.py in log(self, name, value, prog_bar, logger, on_step, on_epoch, reduce_fx, enable_graph, sync_dist, sync_dist_group, add_dataloader_idx, batch_size, metric_attribute, rank_zero_only)
    476             if metric_attribute is None:
    477                 raise MisconfigurationException(
--> 478                     \"Could not find the `LightningModule` attribute for the `torchmetrics.Metric` logged.\"
    479                     f\" You can fix this by calling `self.log({name}, ..., metric_attribute=name)` where `name` is one\"
    480                     f\" of {list(self._metric_attributes.values())}\"

MisconfigurationException: Could not find the `LightningModule` attribute for the `torchmetrics.Metric` logged. You can fix this by calling `self.log(image_AUROC, ..., metric_attribute=name)` where `name` is one of ['image_threshold', 'pixel_threshold', 'image_metrics.AUROC', 'normalization_metrics']"
}

[blaz-r]

Hello. Could you please provide the config.yaml that you used?

[Dpat123]

Dear Sir , We are using your 500_usa_cases\501_dobot - folder with 501_training_a_model_with_cube file i think that does not have any confog.yaml file in it's folder . do we have to add config.yamal to this folder . i am just using your given ipynb file. and as specified by your code callback. Regards, Darshan

…

On Sun, Jan 7, 2024 at 6:28 PM Blaž Rolih ***@***.***> wrote: Hello. Could you please provide the config.yaml that you used? — Reply to this email directly, view it on GitHub <#1573 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/BFCXQ3HGV4YNCHOWSYGWSLTYNKLXPAVCNFSM6AAAAABBLMNNHSVHI2DSMVQWIX3LMV43SRDJONRXK43TNFXW4Q3PNVWWK3TUHM4DAMZWG42TA> . You are receiving this because you authored the thread.Message ID: ***@***.*** com>

-- For SquareOne, Darshan Patel M-9879565952

[blaz-r]

Hi, sorry for the confusion. I see, this is a notebook. I will check it and get back to you as soon as possible.

[blaz-r]

Hi @Dpat123, I tested the notebook and I was not able to reproduce the error you mentioned here.
I'm not sure what exactly the error is, but I recommend that you make a new en environment where you install anomalib using the provided pip inside notebook, or follow this guide with new environment: https://github.com/openvinotoolkit/anomalib#getting-started