Initial

README.md

@@ -26,17 +26,13 @@ VEGA_AE
 |   |──New_Fine_Tuned_Model
 |   └──UnixCoder
 |——Scripts
-|   |──Exp
-|   |   |──Accuracy
-|   |   |──Correction
-|   |   |──ForkFlow
-|   |   |──Performance
-|   |   └──Time
-|   └──UnixCoder
-└──Fixed_VEGA_LLVM
-    |──llvm-riscv
-    |──llvm-ri5cy
-    └──llvm-xcore
+    |──Exp
+    |   |──Accuracy
+    |   |──Correction
+    |   |──ForkFlow
+    |   |──Performance
+    |   └──Time
+    └──UnixCoder
 ```
 ## 2. Hardware Dependency
 

Scripts/UnixCoder/run_one_model.py

@@ -428,8 +428,8 @@ def vega_train_main():
 
     if args.do_train:
         # Prepare training data loader
-        all_examples = read_examples(folder+"/"+args.train_filename, False)
-        train_examples = read_examples_no_bracket(folder+"/"+args.train_filename, False)
+        all_examples = read_examples(args.train_filename, False)
+        train_examples = read_examples_no_bracket(args.train_filename, False)
         train_features = convert_examples_to_features(
             train_examples, tokenizer, args, stage='train')
         all_source_ids = torch.tensor(
@@ -466,7 +466,7 @@ def vega_train_main():
         logger.info("  Num epoch = %d", args.num_train_epochs)
 
         model.train()
-        eval_examples_all = read_examples(folder+"/"+args.dev_filename, False)
+        eval_examples_all = read_examples(args.dev_filename, False)
         total_eval_all = len(eval_examples_all)
         patience, best_acc, losses, dev_dataset = 0, 0, [], {}
         for epoch in tqdm(range(args.num_train_epochs)):
@@ -505,7 +505,7 @@ def vega_train_main():
                 if 'dev_loss' in dev_dataset:
                     eval_examples, eval_data = dev_dataset['dev_loss']
                 else:
-                    eval_examples = read_examples_no_bracket(folder+"/"+args.dev_filename)
+                    eval_examples = read_examples_no_bracket(args.dev_filename)
                     eval_features = convert_examples_to_features(
                         eval_examples, tokenizer, args, stage='dev')
                     all_source_ids = torch.tensor(
@@ -549,7 +549,7 @@ def vega_train_main():
                 if 'dev_acc' in dev_dataset:
                     eval_examples, eval_data = dev_dataset['dev_acc']
                 else:
-                    eval_examples = read_examples_no_bracket(folder+"/"+args.dev_filename) 
+                    eval_examples = read_examples_no_bracket(args.dev_filename) 
                     eval_examples = random.sample(eval_examples, int(len(eval_examples) / divide_number))
                     eval_features = convert_examples_to_features(
                         eval_examples, tokenizer, args, stage='test')