update: inference code

filter/labeling_system.py

@@ -10,7 +10,7 @@ import tty
 import termios
 from sentence_transformers import SentenceTransformer
 from db_utils import fetch_entry_data, parse_entry_data
-from modelV3_4 import VideoClassifierV3_4
+from modelV3_9 import VideoClassifierV3_9
 
 class LabelingSystem:
     def __init__(self, mode='model_testing', database_path="./data/main.db", 
@@ -27,7 +27,7 @@ class LabelingSystem:
         self.model = None
         self.sentence_transformer = None
         if self.mode == 'model_testing':
-            self.model = VideoClassifierV3_4()
+            self.model = VideoClassifierV3_9()
             self.model.load_state_dict(torch.load(model_path))
             self.model.eval()
             self.sentence_transformer = SentenceTransformer("Thaweewat/jina-embedding-v3-m2v-1024")

filter/predict.py

@@ -3,7 +3,7 @@ os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
 import sqlite3
 import json
 import torch
-from modelV3_4 import VideoClassifierV3_4
+from modelV3_9 import VideoClassifierV3_9
 from sentence_transformers import SentenceTransformer
 from tqdm import tqdm  # 导入 tqdm
 
@@ -43,8 +43,8 @@ def parse_entry_data(data):
 
 def initialize_model():
     """初始化模型和文本编码器"""
-    model = VideoClassifierV3_4()
-    model.load_state_dict(torch.load('./filter/checkpoints/best_model_V3.8.pt', map_location=torch.device('cpu')))
+    model = VideoClassifierV3_9()
+    model.load_state_dict(torch.load('./filter/checkpoints/best_model_V3.9.pt', map_location=torch.device('cpu')))
     model.eval()
     
     st_model = SentenceTransformer("Thaweewat/jina-embedding-v3-m2v-1024")

filter/test.py

@@ -1,7 +1,7 @@
 from labeling_system import LabelingSystem
 
 DATABASE_PATH = "./data/main.db"
-MODEL_PATH = "./filter/checkpoints/best_model_V3.8.pt"
+MODEL_PATH = "./filter/checkpoints/best_model_V3.9.pt"
 OUTPUT_FILE = "./data/filter/real_test.jsonl"
 BATCH_SIZE = 50