Small fixes

train.py

@@ -13,12 +13,11 @@ import torch
 
 from itertools import islice
 
-from fairseq import criterions, models, options, progress_bar
+from fairseq import criterions, models, options, progress_bar, utils
 from fairseq.data import data_utils, data_loaders
 from fairseq.fp16_trainer import FP16Trainer
 from fairseq.trainer import Trainer
 from fairseq.meters import AverageMeter, StopwatchMeter
-from fairseq.utils import checkpoint_paths
 
 
 def main(args):
@@ -85,9 +84,9 @@ def main(args):
     max_epoch = args.max_epoch or math.inf
     max_update = args.max_update or math.inf
     lr = trainer.get_lr()
-    first_val_loss = None
     train_meter = StopwatchMeter()
     train_meter.start()
+    valid_losses = [None]
     valid_subsets = args.valid_subset.split(',')
     while lr > args.min_lr and epoch <= max_epoch and trainer.get_num_updates() < max_update:
         # train for one epoch
@@ -290,13 +289,16 @@ def save_checkpoint(args, trainer, epoch, end_of_epoch, val_loss):
         updates % args.save_interval_updates == 0
     )
     checkpoint_conds['checkpoint_best.pt'] = (
-        not hasattr(save_checkpoint, 'best') or val_loss < save_checkpoint.best
+        val_loss is not None and
+        (not hasattr(save_checkpoint, 'best') or val_loss < save_checkpoint.best)
     )
     checkpoint_conds['checkpoint_last.pt'] = True  # keep this last so that it's a symlink
 
-    save_checkpoint.best = min(val_loss, getattr(save_checkpoint, 'best', val_loss))
+    prev_best = getattr(save_checkpoint, 'best', val_loss)
+    if val_loss is not None:
+        save_checkpoint.best = min(val_loss, prev_best)
     extra_state = {
-        'best': save_checkpoint.best,
+        'best': prev_best,
         'end_of_epoch': end_of_epoch,
         'epoch': epoch,
         'val_loss': val_loss,