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)

scripts/dis2dis.slurm

@@ -9,7 +9,7 @@
 #SBATCH --gres=gpu:v100-32gb:4
 
 #SBATCH --exclusive
-#SBATCH --nodes=2
+#SBATCH --nodes=4
 #SBATCH --mem=0
 #SBATCH --time=7-00:00:00
 
@@ -46,7 +46,7 @@ srun m2m.py train \
     --val-in-patterns "$data_root_dir/$in_dir/$val_dirs/$in_files" \
     --val-tgt-patterns "$data_root_dir/$tgt_dir/$val_dirs/$tgt_files" \
     --in-channels 3 --out-channels 3 --norms cosmology.dis --augment \
-    --epochs 1024 --batches 3 --loader-workers 3 --lr 0.0002
+    --epochs 1024 --batches 3 --loader-workers 3 --lr 0.001
 #    --load-state checkpoint.pth
 
 

scripts/vel2vel.slurm

@@ -9,7 +9,7 @@
 #SBATCH --gres=gpu:v100-32gb:4
 
 #SBATCH --exclusive
-#SBATCH --nodes=2
+#SBATCH --nodes=4
 #SBATCH --mem=0
 #SBATCH --time=7-00:00:00
 
@@ -46,7 +46,7 @@ srun m2m.py train \
     --val-in-patterns "$data_root_dir/$in_dir/$val_dirs/$in_files" \
     --val-tgt-patterns "$data_root_dir/$tgt_dir/$val_dirs/$tgt_files" \
     --in-channels 3 --out-channels 3 --norms cosmology.vel --augment \
-    --epochs 1024 --batches 3 --loader-workers 3 --lr 0.0002
+    --epochs 1024 --batches 3 --loader-workers 3 --lr 0.001
 #    --load-state checkpoint.pth