Update app.py

app.py

@@ -107,7 +107,8 @@ def action_to_label(who, action_dict):
 # [DAgger 特征打包器]
 # ==========================================
 class DAggerEncoder:
-    def __init__(self, chunk_size=2048): 
+    def __init__(self, worker_idx, chunk_size=2048): 
+        self.worker_idx = worker_idx  # 记住自己的身份
         self.chunk_size = chunk_size
         self.inputs, self.outputs = [], []
         self.local_pool_dir = f"dagger_pool_{WORKER_ID}"
@@ -126,7 +127,7 @@ class DAggerEncoder:
             self.save_and_upload()
 
     def save_and_upload(self):
-        filename = f"chunk_dagger_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{WORKER_ID}.pkl"
+        filename = f"chunk_dagger_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{WORKER_ID}_W{self.worker_idx}.pkl"
         filepath = os.path.join(self.local_pool_dir, filename)
         
         # 保存本地
@@ -233,22 +234,18 @@ import multiprocessing
 
 # 专门为子进程准备的常驻死循环
 def dagger_worker_process(worker_idx):
-    # 🚀 核心修复：在子进程内部重新 import，确保它一定能找到环境！
     import os
     import time
     import torch
     
-    # 强制当前进程只使用 1 个线程，防止多进程抢占 CPU 导致死锁
     torch.set_num_threads(1)
     os.environ["OMP_NUM_THREADS"] = "1"
     
-    print(f"👷 [Worker {worker_idx}] 启动中，正在加载专属常驻模型...")
-    # 子进程独立加载模型，防止多进程共享 PyTorch 对象的显存/内存冲突
     student_bots = {i: load_model_student(i, STUDENT_MODEL) for i in range(3)}
     teacher_bots = {i: load_model_teacher(i, TEACHER_MODEL) for i in range(3)}
-    encoder = DAggerEncoder(chunk_size=2048)
     
-    print(f"⚡ [Worker {worker_idx}] 加载完毕，全速运转！")
+    # 修改点 3：把 worker_idx 传进去
+    encoder = DAggerEncoder(worker_idx=worker_idx, chunk_size=2048) 
     
     while EVAL_RUNNING:
         try: