diff --git a/main.py b/main.py
index 4681484..61ccde8 100644
--- a/main.py
+++ b/main.py
@@ -80,38 +80,42 @@ def flatten_config(dic, running_key=None, flattened_dict={}):
     model = model.to(device)
 
 # # load data
-# if args.DATA.LOAD_CACHED:
-#     cache_file = f"{args.DATA.SAVE_PATH}/{args.DATA.DATASET}/{args.EXP.IMAGE_FEATURES}_{args.EXP.CLIP_PRETRAINED_DATASET}_{args.EXP.CLIP_MODEL.replace('/','_')}.pt"
-#     dataset_classes, dataset_domains = dh.DATASET_CLASSES[args.DATA.DATASET], dh.DATASET_DOMAINS[args.DATA.DATASET]
-#     assert os.path.exists(cache_file), f"{cache_file} does not exist. To compute embeddings, set DATA.LOAD_CACHED=False"
-#     print(f"Loading cached embeddings from {cache_file}")
-#     train_features, train_labels, train_groups, train_domains, train_filenames, val_features, val_labels, val_groups, val_domains, val_filenames, test_features, test_labels, test_groups, test_domains, test_filenames = load_embeddings(cache_file, args.DATA.DATASET)
-cache_file = f"{args.DATA.SAVE_PATH}/{args.DATA.DATASET}/{args.EXP.IMAGE_FEATURES}_{args.EXP.CLIP_PRETRAINED_DATASET}_{args.EXP.CLIP_MODEL.replace('/','_')}.pt"
-dataset_classes, dataset_domains = dh.DATASET_CLASSES[args.DATA.DATASET], dh.DATASET_DOMAINS[args.DATA.DATASET]
-if os.path.exists(cache_file):
+if args.DATA.LOAD_CACHED:
+    cache_file = "data/CUB/vit14_new.pth"
+    dataset_classes, dataset_domains = dh.DATASET_CLASSES[args.DATA.DATASET], dh.DATASET_DOMAINS[args.DATA.DATASET]
+    assert os.path.exists(cache_file), f"{cache_file} does not exist. To compute embeddings, set DATA.LOAD_CACHED=False"
     print(f"Loading cached embeddings from {cache_file}")
     train_features, train_labels, train_groups, train_domains, train_filenames, val_features, val_labels, val_groups, val_domains, val_filenames, test_features, test_labels, test_groups, test_domains, test_filenames = load_embeddings(cache_file, args.DATA.DATASET)
 else:
-    print(f"Computing embeddings and saving to {cache_file}")
-    trainset, valset, testset = dh.get_dataset(DATASET_NAME, preprocess, biased_val=args.EXP.BIASED_VAL)
-    dataset_classes, dataset_domains = dh.get_class(DATASET_NAME), dh.get_domain(DATASET_NAME)
-    train_loader = torch.utils.data.DataLoader(trainset, batch_size=cfg.DATA.BATCH_SIZE, shuffle=True)
-    val_loader = torch.utils.data.DataLoader(valset, batch_size=cfg.DATA.BATCH_SIZE, shuffle=False)
-    test_loader = torch.utils.data.DataLoader(testset, batch_size=cfg.DATA.BATCH_SIZE, shuffle=False)
-    train_features, train_labels, train_groups, train_domains, train_filenames = get_features(train_loader, model, device, model_type=args.EXP.IMAGE_FEATURES)
-    val_features, val_labels, val_groups, val_domains, val_filenames = get_features(val_loader, model, device, model_type=args.EXP.IMAGE_FEATURES)
-    test_features, test_labels, test_groups, test_domains, test_filenames = get_features(test_loader, model, device, model_type=args.EXP.IMAGE_FEATURES)
-    save_dict = {
-        "train_features": train_features, "train_labels": train_labels, "train_groups": train_groups, "train_domains": train_domains, "train_filenames": train_filenames,
-        "val_features": val_features, "val_labels": val_labels, "val_groups": val_groups, "val_domains": val_domains, "val_filenames": val_filenames,
-        "test_features": test_features, "test_labels": test_labels, "test_groups": test_groups, "test_domains": test_domains, "test_filenames": test_filenames,
-        "seed": args.EXP.SEED
-    }
-    if not os.path.exists(f"{args.DATA.SAVE_PATH}/{args.DATA.DATASET}"):
-        os.makedirs(f"{args.DATA.SAVE_PATH}/{args.DATA.DATASET}")
-    cache_file = f"{args.DATA.SAVE_PATH}/{args.DATA.DATASET}/{args.EXP.IMAGE_FEATURES}_{args.EXP.CLIP_PRETRAINED_DATASET}_{args.EXP.CLIP_MODEL.replace('/','_')}.pt"
-    torch.save(save_dict, cache_file)
-    print(f"Saved CLIP embeddings to {cache_file}")
+    cache_file = "data/CUB/vit14_new.pth"
+    dataset_classes, dataset_domains = dh.DATASET_CLASSES[args.DATA.DATASET], dh.DATASET_DOMAINS[args.DATA.DATASET]
+    if os.path.exists(cache_file):
+        print(f"Loading cached embeddings from {cache_file}")
+        train_features, train_labels, train_groups, train_domains, train_filenames, val_features, val_labels, val_groups, val_domains, val_filenames, test_features, test_labels, test_groups, test_domains, test_filenames = load_embeddings(cache_file, args.DATA.DATASET)
+    else:
+        print(f"Computing embeddings and saving to {cache_file}")
+        trainset, valset, testset = dh.get_dataset(DATASET_NAME, preprocess, biased_val=args.EXP.BIASED_VAL)
+        dataset_classes, dataset_domains = dh.get_class(DATASET_NAME), dh.get_domain(DATASET_NAME)
+        train_loader = torch.utils.data.DataLoader(trainset, batch_size=cfg.DATA.BATCH_SIZE, shuffle=True)
+        val_loader = torch.utils.data.DataLoader(valset, batch_size=cfg.DATA.BATCH_SIZE, shuffle=False)
+        test_loader = torch.utils.data.DataLoader(testset, batch_size=cfg.DATA.BATCH_SIZE, shuffle=False)
+        train_features, train_labels, train_groups, train_domains, train_filenames = get_features(train_loader, model, device, model_type=args.EXP.IMAGE_FEATURES)
+        val_features, val_labels, val_groups, val_domains, val_filenames = get_features(val_loader, model, device, model_type=args.EXP.IMAGE_FEATURES)
+        test_features, test_labels, test_groups, test_domains, test_filenames = get_features(test_loader, model, device, model_type=args.EXP.IMAGE_FEATURES)
+        save_dict = {
+            "train_features": train_features, "train_labels": train_labels, "train_groups": train_groups, "train_domains": train_domains, "train_filenames": train_filenames,
+            "val_features": val_features, "val_labels": val_labels, "val_groups": val_groups, "val_domains": val_domains, "val_filenames": val_filenames,
+            "test_features": test_features, "test_labels": test_labels, "test_groups": test_groups, "test_domains": test_domains, "test_filenames": test_filenames,
+            "seed": args.EXP.SEED
+        }
+        if not os.path.exists(f"{args.DATA.SAVE_PATH}/{args.DATA.DATASET}"):
+            os.makedirs(f"{args.DATA.SAVE_PATH}/{args.DATA.DATASET}")
+        cache_file = "data/CUB/vit14_new.pth"
+        torch.save(save_dict, cache_file)
+        print(f"Saved CLIP embeddings to {cache_file}")
+        old_val_features, old_val_labels, old_val_groups, old_val_domains, old_val_filenames = val_features, val_labels, val_groups, val_domains, val_filenames
+        val_features, val_labels, val_groups, val_domains, val_filenames = val_features[::2], val_labels[::2], val_groups[::2], val_domains[::2], val_filenames[::2]
+        test_features, test_labels, test_groups, test_domains, test_filenames = np.concatenate((test_features, old_val_features[1::2])), np.concatenate((test_labels, old_val_labels[1::2])), np.concatenate((test_groups, old_val_groups[1::2])), np.concatenate((test_domains, old_val_domains[1::2])), np.concatenate((test_filenames, old_val_filenames[1::2]))
 
 if args.METHOD.NORMALIZE:
     train_features /= np.linalg.norm(train_features, axis=-1, keepdims=True)
diff --git a/methods/predictors.py b/methods/predictors.py
index 7a68335..6f322b0 100644
--- a/methods/predictors.py
+++ b/methods/predictors.py
@@ -518,7 +518,7 @@ def predict(self, image_feature):
 #     def predict(self, img_embeddings, label=None):
 #         return self.prompt_learner.predict(img_embeddings.cuda())
 
-from CLIP.clip.simple_tokenizer import SimpleTokenizer as _Tokenizer
+from clip.simple_tokenizer import SimpleTokenizer as _Tokenizer
 from collections import OrderedDict
 
 _tokenizer = _Tokenizer()