ValueError: only one element tensors can be converted to Python scalars

[sonu-gupta]

Hi, I'm trying to run the example code for sentiment classification but it gives me this error. It looks like the issue arises due to the way tensor size is calculated.

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-1-2a229acd2f86> in <module>
    171         valid_dataset=valid_dataset,
    172         callbacks=[es],
--> 173         config=config,
    174     )

~/anaconda3/envs/bertopic/lib/python3.7/site-packages/tez/model/tez.py in fit(self, train_dataset, valid_dataset, config, **kwargs)
    455         for _ in range(self.config.epochs):
    456             self.train_state = enums.TrainingState.EPOCH_START
--> 457             self.train(self.train_loader, losses)
    458             if self.valid_loader and self.config.val_strategy == "epoch":
    459                 self.validate(self.valid_loader)

~/anaconda3/envs/bertopic/lib/python3.7/site-packages/tez/model/tez.py in train(self, data_loader, losses)
    399             self.train_state = enums.TrainingState.TRAIN_STEP_START
    400             loss, metrics = self.train_step(data)
--> 401             losses, monitor = self._update_loss_metrics(losses, loss, metrics, data_loader)
    402             self.train_state = enums.TrainingState.TRAIN_STEP_END
    403             if self.valid_loader and self.config.val_strategy == "batch":

~/anaconda3/envs/bertopic/lib/python3.7/site-packages/tez/model/tez.py in _update_loss_metrics(self, losses, loss, metrics, data_loader)
    366     def _update_loss_metrics(self, losses, loss, metrics, data_loader):
    367         if self.model_state == enums.ModelState.TRAIN:
--> 368             losses.update(loss.item() * self.config.gradient_accumulation_steps, data_loader.batch_size)
    369         else:
    370             losses.update(loss.item(), data_loader.batch_size)

ValueError: only one element tensors can be converted to Python scalars