Fix scheduler stepping and label dtype handling in training loop#153
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SarthakJagota wants to merge 1 commit intoML4SCI:mainfrom
Open
Fix scheduler stepping and label dtype handling in training loop#153SarthakJagota wants to merge 1 commit intoML4SCI:mainfrom
SarthakJagota wants to merge 1 commit intoML4SCI:mainfrom
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This PR introduces two small training stability improvements:
• Replaced
scheduler.step(loss)withscheduler.step()to ensure compatibility with schedulers such as CosineAnnealingWarmRestarts which do not use the loss value.• Replaced
labels.type(torch.LongTensor).to(device)withlabels.long().to(device)to avoid unnecessary CPU tensor creation and ensure consistent device handling.These changes do not modify training behavior conceptually but improve correctness and stability of the training loop.
#152