Minor updates to optical flow ref for consistency (#5654)

* Minor updates to optical flow ref for consistency

* Actually put back name

* linting

references/optical_flow/train.py

@@ -75,7 +75,7 @@ def _evaluate(model, args, val_dataset, *, padder_mode, num_flow_updates=None, b
         sampler=sampler,
         batch_size=batch_size,
         pin_memory=True,
-        num_workers=args.num_workers,
+        num_workers=args.workers,
     )
 
     num_flow_updates = num_flow_updates or args.num_flow_updates
@@ -269,17 +269,17 @@ def main(args):
         sampler=sampler,
         batch_size=args.batch_size,
         pin_memory=True,
-        num_workers=args.num_workers,
+        num_workers=args.workers,
     )
 
     logger = utils.MetricLogger()
 
     done = False
-    for current_epoch in range(args.start_epoch, args.epochs):
-        print(f"EPOCH {current_epoch}")
+    for epoch in range(args.start_epoch, args.epochs):
+        print(f"EPOCH {epoch}")
         if args.distributed:
             # needed on distributed mode, otherwise the data loading order would be the same for all epochs
-            sampler.set_epoch(current_epoch)
+            sampler.set_epoch(epoch)
 
         train_one_epoch(
             model=model,
@@ -291,20 +291,20 @@ def main(args):
         )
 
         # Note: we don't sync the SmoothedValues across processes, so the printed metrics are just those of rank 0
-        print(f"Epoch {current_epoch} done. ", logger)
+        print(f"Epoch {epoch} done. ", logger)
 
         if not args.distributed or args.rank == 0:
             checkpoint = {
                 "model": model_without_ddp.state_dict(),
                 "optimizer": optimizer.state_dict(),
                 "scheduler": scheduler.state_dict(),
-                "epoch": current_epoch,
+                "epoch": epoch,
                 "args": args,
             }
-            torch.save(checkpoint, Path(args.output_dir) / f"{args.name}_{current_epoch}.pth")
+            torch.save(checkpoint, Path(args.output_dir) / f"{args.name}_{epoch}.pth")
             torch.save(checkpoint, Path(args.output_dir) / f"{args.name}.pth")
 
-        if current_epoch % args.val_freq == 0 or done:
+        if epoch % args.val_freq == 0 or done:
             evaluate(model, args)
             model.train()
             if args.freeze_batch_norm:
@@ -319,16 +319,14 @@ def get_args_parser(add_help=True):
         type=str,
         help="The name of the experiment - determines the name of the files where weights are saved.",
     )
-    parser.add_argument(
-        "--output-dir", default="checkpoints", type=str, help="Output dir where checkpoints will be stored."
-    )
+    parser.add_argument("--output-dir", default=".", type=str, help="Output dir where checkpoints will be stored.")
     parser.add_argument(
         "--resume",
         type=str,
         help="A path to previously saved weights. Used to re-start training from, or evaluate a pre-saved model.",
     )
 
-    parser.add_argument("--num-workers", type=int, default=12, help="Number of workers for the data loading part.")
+    parser.add_argument("--workers", type=int, default=12, help="Number of workers for the data loading part.")
 
     parser.add_argument(
         "--train-dataset",