Change ReduceLROnPlateau to CyclicLR

map2map/train.py

@@ -88,7 +88,9 @@ def gpu_worker(local_rank, args):
         #momentum=args.momentum,
         #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,
+            base_lr=args.lr * 1e-2, max_lr=args.lr)
 
     if args.load_state:
         state = torch.load(args.load_state, map_location=args.device)
@@ -117,11 +119,11 @@ def gpu_worker(local_rank, args):
 
     for epoch in range(args.start_epoch, args.epochs):
         train_sampler.set_epoch(epoch)
-        train(epoch, train_loader, model, criterion, optimizer, args)
+        train(epoch, train_loader, model, criterion, optimizer, scheduler, args)
 
         val_loss = validate(epoch, val_loader, model, criterion, args)
 
-        scheduler.step(val_loss)
+        #scheduler.step(val_loss)
 
         if args.rank == 0:
             args.logger.close()
@@ -145,7 +147,7 @@ def gpu_worker(local_rank, args):
     destroy_process_group()
 
 
-def train(epoch, loader, model, criterion, optimizer, args):
+def train(epoch, loader, model, criterion, optimizer, scheduler, args):
     model.train()
 
     for i, (input, target) in enumerate(loader):
@@ -161,6 +163,9 @@ def train(epoch, loader, model, criterion, optimizer, args):
         loss.backward()
         optimizer.step()
 
+        if scheduler is not None:
+            scheduler.step()
+
         batch = epoch * len(loader) + i + 1
         if batch % args.log_interval == 0:
             all_reduce(loss)