Run PTI only if placeholder tokens arg isn't empty

1 files changed, 6 insertions, 6 deletions

diff --git a/train_ti.py b/train_ti.py
index 344b412..c1c0eed 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -602,7 +602,7 @@ def main():
     elif args.mixed_precision == "bf16":
         weight_dtype = torch.bfloat16
 
-    logging.basicConfig(filename=output_dir/"log.txt", level=logging.DEBUG)
+    logging.basicConfig(filename=output_dir / "log.txt", level=logging.DEBUG)
 
     if args.seed is None:
         args.seed = torch.random.seed() >> 32
@@ -743,7 +743,7 @@ def main():
     else:
         raise ValueError(f"Unknown --optimizer \"{args.optimizer}\"")
 
-    checkpoint_output_dir = output_dir/"checkpoints"
+    checkpoint_output_dir = output_dir / "checkpoints"
 
     trainer = partial(
         train,
@@ -782,11 +782,11 @@ def main():
 
     def run(i: int, placeholder_tokens, initializer_tokens, num_vectors, data_template):
         if len(placeholder_tokens) == 1:
-            sample_output_dir = output_dir/f"samples_{placeholder_tokens[0]}"
-            metrics_output_file = output_dir/f"{placeholder_tokens[0]}.png"
+            sample_output_dir = output_dir / f"samples_{placeholder_tokens[0]}"
+            metrics_output_file = output_dir / f"{placeholder_tokens[0]}.png"
         else:
-            sample_output_dir = output_dir/"samples"
-            metrics_output_file = output_dir/f"lr.png"
+            sample_output_dir = output_dir / "samples"
+            metrics_output_file = output_dir / "lr.png"
 
         placeholder_token_ids, initializer_token_ids = add_placeholder_tokens(
             tokenizer=tokenizer,