Merge branch 'master' of https://github.com/tgisaturday/pytorch-image-models into torchamp

train.py

@@ -25,9 +25,12 @@ try:
     from apex.parallel import convert_syncbn_model
     has_apex = True
 except ImportError:
+    from torch.cuda import amp
     from torch.nn.parallel import DistributedDataParallel as DDP
     has_apex = False
  
+    
+
 from timm.data import Dataset, create_loader, resolve_data_config, Mixup, FastCollateMixup, AugMixDataset
 from timm.models import create_model, resume_checkpoint, convert_splitbn_model
 from timm.utils import *
@@ -327,6 +330,10 @@ def main():
     if has_apex and args.amp:
         model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
         use_amp = True
+    elif args.amp:
+        _logger.info('Using torch AMP. Install NVIDIA Apex for Apex AMP.')           
+        scaler = torch.cuda.amp.GradScaler()        
+        use_amp = True        
     if args.local_rank == 0:
         _logger.info('NVIDIA APEX {}. AMP {}.'.format(
             'installed' if has_apex else 'not installed', 'on' if use_amp else 'off'))
@@ -506,7 +513,8 @@ def main():
             train_metrics = train_epoch(
                 epoch, model, loader_train, optimizer, train_loss_fn, args,
                 lr_scheduler=lr_scheduler, saver=saver, output_dir=output_dir,
-                use_amp=use_amp, model_ema=model_ema, mixup_fn=mixup_fn)
+                use_amp=use_amp, has_apex=has_apex, scaler = scaler,
+                model_ema=model_ema, mixup_fn=mixup_fn)
 
             if args.distributed and args.dist_bn in ('broadcast', 'reduce'):
                 if args.local_rank == 0:
@@ -536,7 +544,7 @@ def main():
                 save_metric = eval_metrics[eval_metric]
                 best_metric, best_epoch = saver.save_checkpoint(
                     model, optimizer, args,
-                    epoch=epoch, model_ema=model_ema, metric=save_metric, use_amp=use_amp)
+                    epoch=epoch, model_ema=model_ema, metric=save_metric, use_amp=has_apex&use_amp)
 
     except KeyboardInterrupt:
         pass
@@ -546,7 +554,8 @@ def main():
 
 def train_epoch(
         epoch, model, loader, optimizer, loss_fn, args,
-        lr_scheduler=None, saver=None, output_dir='', use_amp=False, model_ema=None, mixup_fn=None):
+        lr_scheduler=None, saver=None, output_dir='', use_amp=False, 
+        has_apex=False, scaler = None, model_ema=None, mixup_fn=None):
 
     if args.mixup_off_epoch and epoch >= args.mixup_off_epoch:
         if args.prefetcher and loader.mixup_enabled:
@@ -570,19 +579,31 @@ def train_epoch(
             input, target = input.cuda(), target.cuda()
             if mixup_fn is not None:
                 input, target = mixup_fn(input, target)
-
+        if not has_apex and use_amp:
+            with torch.cuda.amp.autocast():
+                output = model(input)
+                loss = loss_fn(output, target)
+        else:
             output = model(input)
-
             loss = loss_fn(output, target)
+            
         if not args.distributed:
             losses_m.update(loss.item(), input.size(0))
 
         optimizer.zero_grad()
         if use_amp:
+            if has_apex:
                 with amp.scale_loss(loss, optimizer) as scaled_loss:
                     scaled_loss.backward()
+            else:
+                scaler.scale(loss).backward()
+                
         else:
             loss.backward()
+        if not has_apex and use_amp:
+            scaler.step(optimizer)
+            scaler.update()
+        else:
             optimizer.step()
 
         torch.cuda.synchronize()
@@ -626,8 +647,9 @@ def train_epoch(
 
         if saver is not None and args.recovery_interval and (
                 last_batch or (batch_idx + 1) % args.recovery_interval == 0):
+            
             saver.save_recovery(
-                model, optimizer, args, epoch, model_ema=model_ema, use_amp=use_amp, batch_idx=batch_idx)
+                model, optimizer, args, epoch, model_ema=model_ema, use_amp=has_apex&use_amp, batch_idx=batch_idx)
 
         if lr_scheduler is not None:
             lr_scheduler.step_update(num_updates=num_updates, metric=losses_m.avg)