remove unused verbose option & make arguments to averaging script nicer

scripts/average_checkpoints.py

@@ -100,22 +100,32 @@ def main():
         help='Write the new checkpoint containing the averaged weights to this '
              'path.',
     )
-    parser.add_argument(
-        '--num',
+    num_group = parser.add_mutually_exclusive_group()
+    num_group.add_argument(
+        '--num-epoch-checkpoints',
         type=int,
         help='if set, will try to find checkpoints with names checkpoint_xx.pt in the path specified by input, '
-             'and average last num of those',
+             'and average last this many of them.',
     )
-    parser.add_argument(
-        '--update-based-checkpoints',
-        action='store_true',
-        help='if set and used together with --num, averages update-based checkpoints instead of epoch-based checkpoints'
+    num_group.add_argument(
+        '--num-update-checkpoints',
+        type=int,
+        help='if set, will try to find checkpoints with names checkpoint_ee_xx.pt in the path specified by input, '
+             'and average last this many of them.',
     )
     args = parser.parse_args()
     print(args)
 
-    if args.num is not None:
-        args.inputs = last_n_checkpoints(args.inputs, args.num, args.update_based_checkpoints)
+    num = None
+    is_update_based = False
+    if args.num_update_checkpoints is not None:
+        num = args.num_update_checkpoints
+        is_update_based = True
+    elif args.num_epoch_checkpoints is not None:
+        num = args.num_epoch_checkpoints
+
+    if num is not None:
+        args.inputs = last_n_checkpoints(args.inputs, num, is_update_based)
         print('averaging checkpoints: ', args.inputs)
 
     new_state = average_checkpoints(args.inputs)

train.py

@@ -203,7 +203,7 @@ def get_training_stats(trainer):
     return stats
 
 
-def validate(args, trainer, dataset, subset, epoch, num_updates, verbose):
+def validate(args, trainer, dataset, subset, epoch, num_updates):
     """Evaluate the model on the validation set and return the average loss."""
 
     # Initialize dataloader
@@ -237,16 +237,6 @@ def validate(args, trainer, dataset, subset, epoch, num_updates, verbose):
     for sample in progress:
         log_output = trainer.valid_step(sample)
 
-        if verbose:
-            # log mid-validation stats
-            stats = get_valid_stats(trainer)
-            for k, v in log_output.items():
-                if k in ['loss', 'nll_loss', 'sample_size']:
-                    continue
-                extra_meters[k].update(v)
-                stats[k] = extra_meters[k].avg
-            progress.log(stats)
-
     # log validation stats
     stats = get_valid_stats(trainer)
     for k, meter in extra_meters.items():
@@ -283,7 +273,7 @@ def val_loss(args, trainer, dataset, epoch, num_updates=None):
     # evaluate on validate set
     subsets = args.valid_subset.split(',')
     # we want to validate all subsets so the results get printed out, but return only the first
-    losses = [validate(args, trainer, dataset, subset, epoch, num_updates, verbose=False) for subset in subsets]
+    losses = [validate(args, trainer, dataset, subset, epoch, num_updates) for subset in subsets]
     return losses[0] if len(losses) > 0 else None