support gsam2 image predictor model

grounding_dino/groundingdino/util/get_tokenlizer.py

@@ -0,0 +1,29 @@
+from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
+import os
+
+def get_tokenlizer(text_encoder_type):
+    if not isinstance(text_encoder_type, str):
+        # print("text_encoder_type is not a str")
+        if hasattr(text_encoder_type, "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))
+            )
+    print("final text_encoder_type: {}".format(text_encoder_type))
+
+    tokenizer = AutoTokenizer.from_pretrained(text_encoder_type)
+    return tokenizer
+
+
+def get_pretrained_language_model(text_encoder_type):
+    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))