Hi, I am a bit confused about metric logging in training_step/validation_step.
Now a standard training_step is
def training_step(self, batch, batch_idx):
labels=<from somewhere>
logits = self.forward(batch)
loss = F.cross_entropy(logits, labels)
with torch.no_grad():
correct = (torch.argmax(logits, dim=1) == labels).sum()
total = len(labels)
acc = (torch.argmax(logits, dim=1) == labels).float().mean()
log = dict(train_loss=loss, train_acc=acc, correct=correct, total=total)
self.log("train_loss", loss, on_step=True, on_epoch=True, prog_bar=True)
self.log("train_acc", acc, on_step=True)
return dict(loss=loss, log=log)
Now here my doubt is, the train_loss that I am logging here, is it the train loss for this particular batch or averaged loos over entire epoch. Now as I have set, on_step and on_epoch both True, what is actually getting logged and when(after each batch or at the epoch end)?
About training_acc, when I have set on_step to True, does it only log the per batch accuracy during training and not the overall epoch accuracy?
Now with this training_step, if I add a custom training_epoch_end like this
def training_epoch_end(self, outputs) -> None:
correct = 0
total = 0
for o in outputs:
correct += o["log"]["correct"]
total += o["log"]["total"]
self.log("train_epoch_acc", correct / total)
Is the train_epoch_acc here same as the average of per batch training_acc?
I intend to put an EarlyStoppingCallBack with monitoring validation loss of the epoch, defined in a same fashion as for train_loss.
If I just put early_stop_callback = pl.callbacks.EarlyStopping(monitor="val_loss", patience=p) , will it monitor per batch val_loss or epoch wise val_loss as logging for val_loss is happening during batch end and epoch end as well.
Sorry if my questions are a little too silly, but I am confused about this!
Thank you!