add: predict for filter model

.gitignore

@@ -74,3 +74,4 @@ filter/runs
 data/filter/eval*
 data/filter/train*
 filter/checkpoints
+data/filter/model_predicted.jsonl

data/filter/labeled_data.jsonl

@@ -914,3 +914,4 @@
 {"aid": 754905929, "title": "【螺旋音リボ sublimation】未来線【多音階CVVC配布】", "description": "螺旋音リボが一周年を迎えました。\n今回配布する音源名は、螺旋音リボ 多音階CVVC sublimationです。デルタ式7モーラ母音優先リストを使用して録音した三音階CVVC音源です。\n収録音階はF#3,A3,A#3です。A#3は強めに録っております。\nsublimationは昇華という意味の英単語です。\n\nノイズ除去を塩釜（mylist/41289446）さんにお願いいたしました。ありがとうございました！\n\nそれではこれからも螺旋音リボをよろしくお願いいたします。\n\n音源ダウンロード: http://gluglu-singer.jimdo.com/\n素敵な本家:　sm15746943\nust: sm16402299\nillustration: 悠みなも　mylist/34371459\nvocal: 螺旋音リボ 多音階CVVC sublimation (cho 螺旋音リボ　単独音)\n\n------------------------------------------------------------------------------------\n我是日本人。我使用Google翻译进行了翻译，但是如果我输入有误，我们深感抱歉。\n\n螺旋音リボ正在庆祝其成立一周年。\n这次要分配的声源名称是螺旋音リボ Multitone CVVC升华。使用delta 7-mora元音优先级列表录制的三音CVVC声源。\n记录的音阶为F＃3，A3，A＃3。强烈记录了＃3。\n升华是英文单词，意为升华。\n\n我请盐og先生（mylist / 41289446）消除噪音。非常感谢你！\n\n感谢您一直以来对螺旋音リボ的支持。\n\n声音源下载：http://gluglu-singer.jimdo.com/\n不错的户主：sm15746943\nUST：sm16402299\n插图：悠みなも mylist / 34371459\nvocal：螺旋音リボ 多音CVVC升华（cho螺旋音リボ 单声）", "tags": ["UTAU", "日本", "ＵＴＡＵ音源配布所リンク", "螺旋音リボ", "VOCALOID→UTAUカバー曲", "未来線"], "author_info": "悠みなも: UTAU/螺旋音リボ/DTM Weibo MINAMO_YU0001", "label": 0}
 {"aid": 685634071, "title": "【AI歌手】腐草为萤【Cover】", "description": "原唱：银临\n引擎：歌叽歌叽\nPV借用：av2700587\nPV：星の祈\n绘：otakucake阿饼（P站：pixiv.me/otakucake）", "tags": ["AI", "歌叽歌叽"], "author_info": "一支粉笔w: 人生难得一知己，千古知音最难求。 | 网易云：粉笔 微博：http://weibo.com/Chalk10", "label": 2}
 {"aid": 3372898, "title": "【言和英文】Counting Stars", "description": "自制 终于投了。1.封面仍然灵魂p图。2.这玩意儿9月底就在做了，说好10月投，pv还解决不了。说好11月投，但又因为曲子本身又拖了。上个星期想投，结果，电脑坏掉加发烧。。。。。所以一路拖到今天才投。算是放下心中的一块石头了吧。3.这次画了很多参数，感觉有提升，不知道朱军是怎么看的。5.counting stars是我最最最喜欢的一首歌，已经循环一年。6.1R赶紧出4专！！！", "tags": ["VOCALOID", "COUNTING STARS", "言和", "ONEREPUBLIC"], "author_info": "JackChenZz: 随缘更新", "label": 0}
+{"aid": 589575, "title": "【洛天依】约定", "description": "绫：天依，不管几年之后你能否记得我，我会一直恋你，爱你，铭心记住你！ QAQ 被小汐撺掇调的，咱非常不擅长这拐来拐去的长调式滴说，不过这次应该算不上渣吧？（好吧- -无视这句）。翻的周慧的《约定》，谱子依旧自己来（其实只是自己重做MIDI了，网上的MIDI做觉得不好用，调子太高），3分43秒的字幕是UP无聊的XXX。PV用慢放的《春来发几只》弄的，剪掉了两组燕子镜头~ ~赶脚两个曲子的BPM同步了", "tags": ["乐正绫", "洛天依", "洛天依翻唱曲", "VOCALOID", "良调教", "约定", "周慧"], "author_info": "星璇の天空: Dr.冥月星璇 成就：不会调教的调教师（U/V/SV/C)，专业咸鱼，不会科研的副研究员，不会编程的系统架构师，不会AI的无人机设计师...", "label": 2}

filter/RunningLogs.txt

@@ -18,4 +18,4 @@ Note
 0324: V3.5-test3 # 用回3.2的FC层试试
 0331: V3.6-test3 # 3.5不太行，我试着调下超参
 0335: V3.7-test3 # 3.6还行，再调超参试试看
-0352: V3.8-test3 # 3.7不行，从3.6的基础重新调
+0414: V3.8-test3 # 3.7不行，从3.6的基础重新调

filter/predict.py

@@ -0,0 +1,139 @@
+import os
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
+import sqlite3
+import json
+import torch
+from modelV3_4 import VideoClassifierV3_4
+from sentence_transformers import SentenceTransformer
+
+# 数据库配置
+DATABASE_PATH = "./data/main.db"
+OUTPUT_FILE = "./data/filter/model_predicted.jsonl"
+BATCH_SIZE = 128  # 批量处理的大小
+
+def fetch_all_aids(conn):
+    """获取数据库中所有符合条件的aid"""
+    cursor = conn.cursor()
+    cursor.execute("SELECT aid FROM bili_info_crawl WHERE status = 'success'")
+    aids = [row[0] for row in cursor.fetchall()]
+    return aids
+
+def fetch_entry_data(conn, aid):
+    """获取单个条目的原始数据"""
+    cursor = conn.cursor()
+    cursor.execute("SELECT data FROM bili_info_crawl WHERE aid = ?", (aid,))
+    d = cursor.fetchone()
+    data = d[0] if d else None
+    return data
+
+def parse_entry_data(data):
+    """解析原始数据为结构化信息"""
+    try:
+        obj = json.loads(data)
+        title = obj["View"]["title"]
+        description = obj["View"]["desc"]
+        tags = [tag["tag_name"] for tag in obj["Tags"] 
+               if tag["tag_type"] in ["old_channel", "topic"]]
+        author_info = f"{obj['Card']['card']['name']}: {obj['Card']['card']['sign']}"
+        return title, description, tags, author_info
+    except (KeyError, json.JSONDecodeError) as e:
+        print(f"解析错误: {e}")
+        return None, None, None, None
+
+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.eval()
+    
+    st_model = SentenceTransformer("Thaweewat/jina-embedding-v3-m2v-1024")
+    return model, st_model
+
+def predict_batch(model, st_model, batch_data):
+    """批量执行预测"""
+    with torch.no_grad():
+        input_texts = {
+            "title": [entry["title"] for entry in batch_data],
+            "description": [entry["description"] for entry in batch_data],
+            "tags": [" ".join(entry["tags"]) for entry in batch_data],
+            "author_info": [entry["author_info"] for entry in batch_data]
+        }
+        logits = model(input_texts=input_texts, sentence_transformer=st_model)
+        return torch.argmax(logits, dim=1).tolist()
+
+def process_entries():
+    """主处理流程"""
+    # 初始化模型
+    model, st_model = initialize_model()
+    
+    # 获取数据库连接
+    conn = sqlite3.connect(DATABASE_PATH)
+    
+    # 获取所有aid
+    aids = fetch_all_aids(conn)
+    print(f"总需处理条目数: {len(aids)}")
+
+    # 批量处理并保存结果
+    with open(OUTPUT_FILE, "w", encoding="utf-8") as output:
+        batch_data = []
+        for idx, aid in enumerate(aids, 1):
+            try:
+                # 获取并解析数据
+                raw_data = fetch_entry_data(conn, aid)
+                if not raw_data:
+                    continue
+                    
+                title, desc, tags, author = parse_entry_data(raw_data)
+                if not title:
+                    continue
+
+                # 构造预测输入
+                entry = {
+                    "aid": aid,
+                    "title": title,
+                    "description": desc,
+                    "tags": tags,
+                    "author_info": author
+                }
+                batch_data.append(entry)
+
+                # 当达到批量大小时进行预测
+                if len(batch_data) >= BATCH_SIZE:
+                    predictions = predict_batch(model, st_model, batch_data)
+                    for entry, prediction in zip(batch_data, predictions):
+                        output.write(json.dumps({
+                            "aid": entry["aid"],
+                            "title": entry["title"],
+                            "description": entry["description"],
+                            "tags": entry["tags"],
+                            "author_info": entry["author_info"],
+                            "label": prediction
+                        }, ensure_ascii=False) + "\n")
+                    batch_data = []  # 清空批量数据
+
+                # 进度显示
+                if idx % 100 == 0:
+                    print(f"已处理 {idx}/{len(aids)} 条...")
+                    
+            except Exception as e:
+                print(f"处理aid {aid} 时出错: {str(e)}")
+
+        # 处理剩余的条目
+        if batch_data:
+            predictions = predict_batch(model, st_model, batch_data)
+            for entry, prediction in zip(batch_data, predictions):
+                output.write(json.dumps({
+                    "aid": entry["aid"],
+                    "title": entry["title"],
+                    "description": entry["description"],
+                    "tags": entry["tags"],
+                    "author_info": entry["author_info"],
+                    "label": prediction
+                }, ensure_ascii=False) + "\n")
+
+    # 关闭数据库连接
+    conn.close()
+
+if __name__ == "__main__":
+    process_entries()
+    print("预测完成，结果已保存至", OUTPUT_FILE)

filter/tag.py

@@ -86,8 +86,6 @@ def label_entries(db_path, aids):
         title, description, tags, author_info, url = parse_entry_data(data)
         if not title:  # 如果解析失败，跳过
             continue
-        if '原创' not in title and '原创' not in description:
-            continue
         # 展示信息
         os.system("clear")
         print(f"AID: {aid}")

filter/test.py

@@ -0,0 +1,38 @@
+import os
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"]="1"
+import torch
+from modelV3_4 import VideoClassifierV3_4
+from sentence_transformers import SentenceTransformer
+
+def predict(json_input):
+    # 加载模型
+    model = VideoClassifierV3_4()
+    model.load_state_dict(torch.load('./filter/checkpoints/best_model_V3.8.pt'))
+    model.eval()
+    
+    # 加载SentenceTransformer
+    sentence_transformer = SentenceTransformer("Thaweewat/jina-embedding-v3-m2v-1024")
+
+    input_texts = {
+        "title": [json_input["title"]],
+        "description": [json_input["description"]],
+        "tags": [" ".join(json_input["tags"])],
+        "author_info": [json_input["author_info"]]
+    }
+    
+    # 预测
+    with torch.no_grad():
+        logits = model(
+            input_texts=input_texts,
+            sentence_transformer=sentence_transformer
+        )
+        pred = torch.argmax(logits, dim=1).item()
+            
+    return pred
+
+if __name__ == "__main__":
+    # 示例用法
+    sample_input = {"title": "", "description": "", "tags": ["",""], "author_info": "xx: yy"}
+    
+    result = predict(sample_input)
+    print(f"预测结果: {result}")