Comments (4)
It should probably crash and if people want to train without eval samples, they can skip it with run_eval = False
from trainer.
Not sure where to raise the error though.
If we put it in the __init__
here, we risk raising an error when the samples were actually being loaded in model.get_data_loader()
.
But if we check after the get_eval_dataloader here, when no eval sample are provided it can crash if the model.get_data_loader
doesn't support no samples being passed to it.
For example gan dataset here throws this without eval samples.
Traceback (most recent call last):
File "/home/julian/workspace/Trainer/trainer/trainer.py", line 1467, in fit
self._fit()
File "/home/julian/workspace/Trainer/trainer/trainer.py", line 1453, in _fit
self.eval_epoch()
File "/home/julian/workspace/Trainer/trainer/trainer.py", line 1314, in eval_epoch
self.get_eval_dataloader(
File "/home/julian/workspace/Trainer/trainer/trainer.py", line 777, in get_eval_dataloader
return self._get_loader(
File "/home/julian/workspace/Trainer/trainer/trainer.py", line 700, in _get_loader
loader = model.get_data_loader(
File "/home/julian/workspace/TTS/TTS/vocoder/models/gan.py", line 333, in get_data_loader
dataset = GANDataset(
File "/home/julian/workspace/TTS/TTS/vocoder/datasets/gan_dataset.py", line 36, in __init__
self.compute_feat = not isinstance(items[0], (tuple, list))
TypeError: 'NoneType' object is not subscriptable
Where should we do this in your mind @erogol ?
from trainer.
maybe we can use an empty list as the default for the samples and check the data loader size. If it is zero, raise an error.
from trainer.
We'd still have the same problem, since item[0] would raise out if range right?
from trainer.
Related Issues (20)
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from trainer.