Error on model.fit while training on custom Dataset (v1) without validation and test

[CHHA01]

Hello,

I am trying to run the Fastflow model on custom dataset with just good images.

Here is my code

datamodule = Folder(
    name="coils_data",
    root=root_dir,
    num_workers = 4,
    image_size = image_size,
    normal_dir="coil",
    task = "segmentation",
    test_split_mode = "none",
    val_split_mode="none"
)

datamodule.setup()

i, train_data = next(enumerate(datamodule.train_dataloader()))

model = Patchcore(backbone="resnet18")

callbacks = [
    ModelCheckpoint(

        mode="max",
        dirpath = "<path for model checkpoint>",
        verbose = True,
        monitor="pixel_AUROC",
    ),
    EarlyStopping(
        monitor="pixel_AUROC",
        mode="max",
        patience=3,
    ),
]

engine = Engine(
    callbacks=callbacks,
    pixel_metrics="AUROC",
    accelerator="cuda",  # \<"cpu", "gpu", "tpu", "ipu", "hpu", "auto">,
    devices=1,
    logger=False,
)

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

I get the following error

AttributeError                            Traceback (most recent call last)
Cell In[15], line 1
----> 1 engine.fit(datamodule=datamodule, model=model)

File [~/hardik/anomalib/anomalib/src/anomalib/engine/engine.py:516](http://localhost:1238/lab/tree/hardik/anomalib/anomalib/hardik/anomalib/anomalib/src/anomalib/engine/engine.py#line=515), in Engine.fit(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
    513     self.trainer.validate(model, val_dataloaders, datamodule=datamodule, ckpt_path=ckpt_path)
    515 else:
--> 516     self.trainer.fit(model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)

File [~/anaconda3/envs/anomalib_env/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py:544](http://localhost:1238/lab/tree/hardik/anomalib/anomalib/anaconda3/envs/anomalib_env/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py#line=543), in Trainer.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 )

File [~/anaconda3/envs/anomalib_env/lib/python3.10/site-packages/lightning/pytorch/trainer/call.py:44](http://localhost:1238/lab/tree/hardik/anomalib/anomalib/anaconda3/envs/anomalib_env/lib/python3.10/site-packages/lightning/pytorch/trainer/call.py#line=43), 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)
     46 except _TunerExitException:
     47     _call_teardown_hook(trainer)

File [~/anaconda3/envs/anomalib_env/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py:580](http://localhost:1238/lab/tree/hardik/anomalib/anomalib/anaconda3/envs/anomalib_env/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py#line=579), in Trainer._fit_impl(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
    573 assert self.state.fn is not None
    574 ckpt_path = self._checkpoint_connector._select_ckpt_path(
    575     self.state.fn,
    576     ckpt_path,
    577     model_provided=True,
    578     model_connected=self.lightning_module is not None,
    579 )
--> 580 self._run(model, ckpt_path=ckpt_path)
    582 assert self.state.stopped
    583 self.training = False

File [~/anaconda3/envs/anomalib_env/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py:989](http://localhost:1238/lab/tree/hardik/anomalib/anomalib/anaconda3/envs/anomalib_env/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py#line=988), in Trainer._run(self, model, ckpt_path)
    984 self._signal_connector.register_signal_handlers()
    986 # ----------------------------
    987 # RUN THE TRAINER
    988 # ----------------------------
--> 989 results = self._run_stage()
    991 # ----------------------------
    992 # POST-Training CLEAN UP
    993 # ----------------------------
    994 log.debug(f"{self.__class__.__name__}: trainer tearing down")

File [~/anaconda3/envs/anomalib_env/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py:1035](http://localhost:1238/lab/tree/hardik/anomalib/anomalib/anaconda3/envs/anomalib_env/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py#line=1034), in Trainer._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}")

File [~/anaconda3/envs/anomalib_env/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py:198](http://localhost:1238/lab/tree/hardik/anomalib/anomalib/anaconda3/envs/anomalib_env/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py#line=197), in _FitLoop.run(self)
    196     return
    197 self.reset()
--> 198 self.on_run_start()
    199 while not self.done:
    200     try:

File [~/anaconda3/envs/anomalib_env/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py:320](http://localhost:1238/lab/tree/hardik/anomalib/anomalib/anaconda3/envs/anomalib_env/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py#line=319), in _FitLoop.on_run_start(self)
    318 if self.epoch_loop._should_check_val_epoch() and trainer.val_dataloaders is None:
    319     trainer.validating = True
--> 320     self.epoch_loop.val_loop.setup_data()
    321     trainer.training = True
    323 call._call_callback_hooks(trainer, "on_train_start")

File [~/anaconda3/envs/anomalib_env/lib/python3.10/site-packages/lightning/pytorch/loops/evaluation_loop.py:165](http://localhost:1238/lab/tree/hardik/anomalib/anomalib/anaconda3/envs/anomalib_env/lib/python3.10/site-packages/lightning/pytorch/loops/evaluation_loop.py#line=164), in _EvaluationLoop.setup_data(self)
    163 stage = self._stage
    164 source = self._data_source
--> 165 dataloaders = _request_dataloader(source)
    166 trainer.strategy.barrier(f"{stage.dataloader_prefix}_dataloader()")
    168 if not isinstance(dataloaders, CombinedLoader):

File [~/anaconda3/envs/anomalib_env/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:342](http://localhost:1238/lab/tree/hardik/anomalib/anomalib/anaconda3/envs/anomalib_env/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py#line=341), in _request_dataloader(data_source)
    331 """Requests a dataloader by calling dataloader hooks corresponding to the given stage.
    332 
    333 Returns:
    334     The requested dataloader
    335 
    336 """
    337 with _replace_dunder_methods(DataLoader, "dataset"), _replace_dunder_methods(BatchSampler):
    338     # under this context manager, the arguments passed to `DataLoader.__init__` will be captured and saved as
    339     # attributes on the instance in case the dataloader needs to be re-instantiated later by Lightning.
    340     # Also, it records all attribute setting and deletion using patched `__setattr__` and `__delattr__`
    341     # methods so that the re-instantiated object is as close to the original as possible.
--> 342     return data_source.dataloader()

File [~/anaconda3/envs/anomalib_env/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:309](http://localhost:1238/lab/tree/hardik/anomalib/anomalib/anaconda3/envs/anomalib_env/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py#line=308), in _DataLoaderSource.dataloader(self)
    307 if isinstance(self.instance, pl.LightningDataModule):
    308     assert self.instance.trainer is not None
--> 309     return call._call_lightning_datamodule_hook(self.instance.trainer, self.name)
    310 assert self.instance is not None
    311 return self.instance

File [~/anaconda3/envs/anomalib_env/lib/python3.10/site-packages/lightning/pytorch/trainer/call.py:179](http://localhost:1238/lab/tree/hardik/anomalib/anomalib/anaconda3/envs/anomalib_env/lib/python3.10/site-packages/lightning/pytorch/trainer/call.py#line=178), in _call_lightning_datamodule_hook(trainer, hook_name, *args, **kwargs)
    177 if callable(fn):
    178     with trainer.profiler.profile(f"[LightningDataModule]{trainer.datamodule.__class__.__name__}.{hook_name}"):
--> 179         return fn(*args, **kwargs)
    180 return None

File [~/hardik/anomalib/anomalib/src/anomalib/data/base/datamodule.py:219](http://localhost:1238/lab/tree/hardik/anomalib/anomalib/hardik/anomalib/anomalib/src/anomalib/data/base/datamodule.py#line=218), in AnomalibDataModule.val_dataloader(self)
    216 def val_dataloader(self) -> EVAL_DATALOADERS:
    217     """Get validation dataloader."""
    218     return DataLoader(
--> 219         dataset=self.val_data,
    220         shuffle=False,
    221         batch_size=self.eval_batch_size,
    222         num_workers=self.num_workers,
    223         collate_fn=collate_fn,
    224     )

AttributeError: 'Folder' object has no attribute 'val_data'

If I set the test_split_mode to synthetic that works as there is a validation set generated.

Can you help on training my model without validation ?

Since I will receive my mask and lables later , can you also tell me how to adpat the mask threshold when I get the lables