TypeError: 'Subset' object is not callable

[talhaanwarch]

I am getting this weird error TypeError: 'Subset' object is not callable

class OurModel(LightningModule):
    def __init__(self):
        super(OurModel,self).__init__()
        #architecute
        self.model =  timm.create_model('efficientnetv2_rw_s', pretrained=True)
        self.dataset=torchvision.datasets.ImageFolder('./PSL DATA SET')

        self.train, self.val, self.test = torch.utils.data.random_split(self.dataset, [1009, 250, 250])

    def forward(self,x):
        x= self.model(x)
        return x

    def val_dataloader(self):
        print('val_dataloader')
        ds=DataLoader(MyLazyDataset(self.val,aug), batch_size = self.batch_size,num_workers=self.numworker, shuffle=False)
        print(len(ds))
        return ds

    def validation_step(self,batch,batch_idx):
        print('validation step')
        image,label=batch
        out=self(image).squeeze(1)
        loss=self.criterion(out,label.float())
        self.log('val_loss', loss)#use by early stopping
        return loss

print statement shows len(ds) but it does not print 'validation step', so data from val_dataloader is not moving to validation_step

Complete logs

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-7-8edf32f6d94d> in <module>
      8 #trainer.tune(model)
      9 
---> 10 trainer.fit(model)

/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in fit(self, model, train_dataloader, val_dataloaders, datamodule)
    458         )
    459 
--> 460         self._run(model)
    461 
    462         assert self.state.stopped

/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in _run(self, model)
    756 
    757         # dispatch `start_training` or `start_evaluating` or `start_predicting`
--> 758         self.dispatch()
    759 
    760         # plugin will finalized fitting (e.g. ddp_spawn will load trained model)

/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in dispatch(self)
    797             self.accelerator.start_predicting(self)
    798         else:
--> 799             self.accelerator.start_training(self)
    800 
    801     def run_stage(self):

/opt/conda/lib/python3.7/site-packages/pytorch_lightning/accelerators/accelerator.py in start_training(self, trainer)
     94 
     95     def start_training(self, trainer: 'pl.Trainer') -> None:
---> 96         self.training_type_plugin.start_training(trainer)
     97 
     98     def start_evaluating(self, trainer: 'pl.Trainer') -> None:

/opt/conda/lib/python3.7/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py in start_training(self, trainer)
    142     def start_training(self, trainer: 'pl.Trainer') -> None:
    143         # double dispatch to initiate the training loop
--> 144         self._results = trainer.run_stage()
    145 
    146     def start_evaluating(self, trainer: 'pl.Trainer') -> None:

/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in run_stage(self)
    807         if self.predicting:
    808             return self.run_predict()
--> 809         return self.run_train()
    810 
    811     def _pre_training_routine(self):

/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in run_train(self)
    842             self.progress_bar_callback.disable()
    843 
--> 844         self.run_sanity_check(self.lightning_module)
    845 
    846         self.checkpoint_connector.has_trained = False

/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in run_sanity_check(self, ref_model)
   1110 
   1111             # run eval step
-> 1112             self.run_evaluation()
   1113 
   1114             self.on_sanity_check_end()

/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in run_evaluation(self, on_epoch)
    930 
    931         # enable eval mode + no grads
--> 932         self.evaluation_loop.on_evaluation_model_eval()
    933         # ref model
    934         model = self.lightning_module

/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/evaluation_loop.py in on_evaluation_model_eval(self)
     87             model_ref.on_test_model_eval()
     88         else:
---> 89             model_ref.on_validation_model_eval()
     90 
     91     def on_evaluation_model_train(self) -> None:

/opt/conda/lib/python3.7/site-packages/pytorch_lightning/core/hooks.py in on_validation_model_eval(self)
    195         Sets the model to eval during the val loop
    196         """
--> 197         self.trainer.model.eval()
    198 
    199     def on_validation_model_train(self) -> None:

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in eval(self)
   1291             Module: self
   1292         """
-> 1293         return self.train(False)
   1294 
   1295     def requires_grad_(self: T, requires_grad: bool = True) -> T:

[talhaanwarch]

Reason train is a method thats why its throwing this error. Renaming self.train to self.trainx resolved the error. but I think logs should print this message