优化fed_run函数中的进度条显示

fed_run.py

@@ -142,7 +142,8 @@ def fed_run():
     # --- build clients ---
     model_name = cfg.get("model_name", "yolo_v11_n")
     clients = {}
-    for uid in users:
+
+    for uid in tqdm(users, desc="Building clients", leave=True, unit="client"):
         c = FedYoloClient(name=uid, model_name=model_name, params=params)
         c.load_trainset(user_data[uid]["filename"])
         clients[uid] = c
@@ -176,11 +177,11 @@ def fed_run():
     res_root = cfg.get("res_root", "results")
     os.makedirs(res_root, exist_ok=True)
 
-    for rnd in tqdm(range(num_round), desc="main federal loop round"):
+    for rnd in tqdm(range(num_round), desc="main federal loop round:"):
         t0 = time.time()
 
         # Local training (sequential over all users)
-        for uid in tqdm(users, desc=f"Round {rnd + 1} local training", leave=False):
+        for uid in tqdm(users, desc=f"Round {rnd + 1} local training: ", leave=False):
             client = clients[uid]  # FedYoloClient instance
             client.update(global_state)  # load global weights
             state_dict, n_data, loss_dict = client.train(args_cli)  # local training
@@ -213,10 +214,11 @@ def fed_run():
         history["train_loss"].append(scalar_train_loss)
         history["round_time_sec"].append(time.time() - t0)
 
-        print(
+        tqdm.write(
             f"[round {rnd + 1:04d}] "
             f"loss={scalar_train_loss:.4f}  mAP50-95={mAP:.4f}  mAP50={mAP50:.4f}  "
             f"P={precision:.4f}  R={recall:.4f}"
+            f"\n"
         )
 
         # Save running JSON (resumable logs)