Aquila-Consortium/map2map
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Revert scheduler to ReduceLROnPlateau

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This commit is contained in:
Yin Li 2019-12-08 20:58:46 -05:00
parent 34b8c62c76
commit f2e9af6d5f
1 changed files with 5 additions and 6 deletions
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11
map2map/train.py
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@ -88,9 +88,8 @@ def gpu_worker(local_rank, args):
#momentum=args.momentum, #momentum=args.momentum,
#weight_decay=args.weight_decay #weight_decay=args.weight_decay
) )
#scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer) scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
scheduler = torch.optim.lr_scheduler.CyclicLR(optimizer, factor=0.1, verbose=True)
base_lr=args.lr * 1e-2, max_lr=args.lr, cycle_momentum=False)
if args.load_state: if args.load_state:
state = torch.load(args.load_state, map_location=args.device) state = torch.load(args.load_state, map_location=args.device)
@ -123,7 +122,7 @@ def gpu_worker(local_rank, args):
val_loss = validate(epoch, val_loader, model, criterion, args) val_loss = validate(epoch, val_loader, model, criterion, args)
#scheduler.step(val_loss) scheduler.step(val_loss)
if args.rank == 0: if args.rank == 0:
args.logger.close() args.logger.close()
@ -163,8 +162,8 @@ def train(epoch, loader, model, criterion, optimizer, scheduler, args):
loss.backward() loss.backward()
optimizer.step() optimizer.step()
if scheduler is not None: #if scheduler is not None: # for batch scheduler
scheduler.step() #scheduler.step()
batch = epoch * len(loader) + i + 1 batch = epoch * len(loader) + i + 1
if batch % args.log_interval == 0: if batch % args.log_interval == 0: