<Feat>: use local transformer model (#110)

<Detail>:

<Footer>:

groundingdino/util/get_tokenlizer.py

@@ -1,5 +1,5 @@
 from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
-
+import os
 
 def get_tokenlizer(text_encoder_type):
     if not isinstance(text_encoder_type, str):
@@ -8,6 +8,8 @@ def get_tokenlizer(text_encoder_type):
             text_encoder_type = text_encoder_type.text_encoder_type
         elif text_encoder_type.get("text_encoder_type", False):
             text_encoder_type = text_encoder_type.get("text_encoder_type")
+        elif os.path.isdir(text_encoder_type) and os.path.exists(text_encoder_type):
+            pass
         else:
             raise ValueError(
                 "Unknown type of text_encoder_type: {}".format(type(text_encoder_type))
@@ -19,8 +21,9 @@ def get_tokenlizer(text_encoder_type):
 
 
 def get_pretrained_language_model(text_encoder_type):
-    if text_encoder_type == "bert-base-uncased":
+    if text_encoder_type == "bert-base-uncased" or (os.path.isdir(text_encoder_type) and os.path.exists(text_encoder_type)):
         return BertModel.from_pretrained(text_encoder_type)
     if text_encoder_type == "roberta-base":
         return RobertaModel.from_pretrained(text_encoder_type)
+
     raise ValueError("Unknown text_encoder_type {}".format(text_encoder_type))