Fix for #188

apex/amp/frontend.py

@@ -269,7 +269,7 @@ def initialize(
         https://github.com/NVIDIA/apex/tree/master/examples/imagenet
     """
     _amp_state.opt_properties = Properties()
-    _amp_state.opt_properties.verbosity = verbosity
+    _amp_state.verbosity = verbosity
 
     if not enabled:
         return models, optimizers

examples/imagenet/main_amp.py

@@ -334,8 +334,6 @@ def train(train_loader, model, criterion, optimizer, epoch):
         optimizer.step()
         if args.prof: torch.cuda.nvtx.range_pop()
 
-        input, target = prefetcher.next()
-
         if i%args.print_freq == 0:
             # Every print_freq iterations, check the loss accuracy and speed.
             # For best performance, it doesn't make sense to print these metrics every
@@ -374,6 +372,8 @@ def train(train_loader, model, criterion, optimizer, epoch):
                        batch_time=batch_time,
                        loss=losses, top1=top1, top5=top5))
 
+        input, target = prefetcher.next()
+
 
 def validate(val_loader, model, criterion):
     batch_time = AverageMeter()

tests/L1/common/main_amp.py

@@ -365,6 +365,9 @@ def train(train_loader, model, criterion, optimizer, epoch):
         batch_time.update(time.time() - end)
 
         end = time.time()
+
+        # If you decide to refactor this test, like examples/imagenet, to sample the loss every
+        # print_freq iterations, make sure to move this prefetching below the accuracy calculation.
         input, target = prefetcher.next()
 
         if i % args.print_freq == 0 and i > 1: