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Hi, thanks for making your code public. It is really great work!
I ran your code, and i think there is a minor error in your code.
On line 217 in train.py
lr_scheduler = WarmupCosineAnnealingLR(optimizer=optimizer, T_cosine_max=args.epochs * IMAGENET_TRAINSET_SIZE // args.batch_size // ngpus_per_node, warmup=args.epochs/24)
I think it makes warm up the learning rate during only 5 steps, not 5 epochs. To warm up the learning rate during 5 epochs, 'args.epochs/24' should be 'args.epochs*len(train_loader)/24'.
Therefore, I modify line 217 as follows,
lr_scheduler = WarmupCosineAnnealingLR(optimizer=optimizer, T_cosine_max=args.epochs * len(train_loader), warmup=args.epochs * len(train_loader) / 24)
Thank you!
The text was updated successfully, but these errors were encountered:
Hi, thanks for making your code public. It is really great work!
I ran your code, and i think there is a minor error in your code.
On line 217 in train.py
lr_scheduler = WarmupCosineAnnealingLR(optimizer=optimizer, T_cosine_max=args.epochs * IMAGENET_TRAINSET_SIZE // args.batch_size // ngpus_per_node, warmup=args.epochs/24)
I think it makes warm up the learning rate during only 5 steps, not 5 epochs. To warm up the learning rate during 5 epochs, 'args.epochs/24' should be 'args.epochs*len(train_loader)/24'.
Therefore, I modify line 217 as follows,
lr_scheduler = WarmupCosineAnnealingLR(optimizer=optimizer, T_cosine_max=args.epochs * len(train_loader), warmup=args.epochs * len(train_loader) / 24)
Thank you!
The text was updated successfully, but these errors were encountered: