fix : config

image_ref/main_ray.py

@@ -24,7 +24,7 @@ def train_model(config,args):
                                                   batch_size=args.batch_size,
                                                   ref_dir=args.dataset_ref_dir,
                                                   noise_threshold=config['noise'],
-                                                  positive_prop=config['positive_prop'], sampler=config['sampler'])
+                                                  positive_prop=config['p_prop'], sampler=config['sampler'])
 
     # load model
     model = Classification_model_duo_contrastive(model=args.model, n_class=2)
@@ -142,7 +142,7 @@ def train_model(config,args):
             checkpoint = Checkpoint.from_directory(temp_checkpoint_dir)
             print(checkpoint.path)
             train.report(
-                {"train loss": losses_train, "train contrastive acc": acc_train,"val loss": losses_val,"val acc": acc_val,"val contrastive acc": acc_contrastive_val,},
+                {"train loss": losses_train, "train cont acc": acc_train,"val loss": losses_val,"val acc": acc_val,"val cont acc": acc_contrastive_val,},
                 checkpoint=checkpoint,)
     print("Finished Training")
 
@@ -155,7 +155,7 @@ def test_model(best_result, args):
                                          batch_size=args.batch_size,
                                          ref_dir=args.dataset_ref_dir,
                                          noise_threshold=best_result.config['noise'],
-                                         positive_prop=best_result.config['positive_prop'], sampler=best_result.config['sampler'])
+                                         positive_prop=best_result.config['p_prop'], sampler=best_result.config['sampler'])
 
     # load model
     model = Classification_model_duo_contrastive(model=args.model, n_class=2)
@@ -213,13 +213,13 @@ def main(args, gpus_per_trial=1):
     config = {
         "lr": tune.loguniform(1e-4, 1e-2),
         "noise": tune.loguniform(1, 1000),
-        "positive_prop": tune.uniform(0, 100),
+        "p_prop": tune.uniform(5, 95),
         "optimizer": tune.choice(['Adam', 'SGD']),
         "sampler": tune.choice(['random', 'balanced']),
     }
     scheduler = ASHAScheduler(
         max_t=100,
-        grace_period=20,
+        grace_period=3,
         reduction_factor=3,
         brackets=1,
     )