Comments (4)
Hi @mrpositron, thank you for pointing this out. Could you let me know which version of torch
you're currently using?
from torchdyn.
PyTorch version is 1.6. I ran it on colab. Furthermore, changing
sched = {'scheduler': torch.optim.lr_scheduler.ReduceLROnPlateau(opt),
'monitor': 'loss',
'interval': 'step',
'frequency': 10 }
to
sched = {'scheduler': torch.optim.lr_scheduler.ReduceLROnPlateau(opt),
'monitor': 'train_loss',
'interval': 'step',
'frequency': 10 }
does not result in error.
from torchdyn.
Make sure to have the metric you choose to monitor
available to the LightningModule
after each step. On my end the training loop works with both loss
and train_loss
(pytorch-lightning==0.9.0
).
The following step method:
def training_step(self, batch, batch_idx):
self.iters += 1.
x, y = batch
x, y = x.to(device), y.to(device)
y_hat = self.model(x)
loss = nn.CrossEntropyLoss()(y_hat, y)
epoch_progress = self.iters / self.loader_len
acc = accuracy(y_hat, y)
nfe = model[1].nfe ; model[1].nfe = 0
tqdm_dict = {'train_loss': loss, 'accuracy': acc, 'NFE': nfe}
logs = {'train_loss': loss, 'epoch': epoch_progress}
return {'loss': loss, 'progress_bar': tqdm_dict, 'log': logs}
Should have loss
available. Could you confirm that the MisconfigurationException
is raised even with the above?
from torchdyn.
The new version of pytorch-lightning
has fixed the issue, closing.
from torchdyn.
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from torchdyn.