更新配置文件路径，优化文档字符串，增强代码可读性

utils/fed_util.py

@@ -7,6 +7,7 @@ import numpy as np
 import torch
 from collections import defaultdict
 from typing import Dict, List, Optional, Set, Any
+import time
 
 from nets import nn
 from nets import YOLO
@@ -30,8 +31,10 @@ def _parse_yolo_label_file(label_path: str) -> Set[int]:
     Return a set of class_ids found in a YOLO .txt label file.
     Empty file -> empty set. Missing file -> empty set.
     Robust to blank lines / trailing spaces.
+
     Args:
         label_path: path to the label file
+        
     Returns:
         set of class IDs (integers) found in the file
     """
@@ -85,7 +88,7 @@ def divide_trainset(
     Build a federated split from a YOLO dataset list file.
 
     Args:
-        trainset_path: path to a .txt file containing one image path per line
+        trainset_path (str): path to a .txt file containing one image path per line
                        e.g. /COCO/images/train2017/1111.jpg
         num_local_class: how many distinct classes to sample for each client
         num_client: number of clients
@@ -95,7 +98,9 @@ def divide_trainset(
               "disjoint" -> each image is used by at most one client
         seed: optional random seed for reproducibility
 
-    Returns:
+    Returns::
+
+    >>> \\
         trainset_divided = {
             "users": ["c_00001", ...],
             "user_data": {
@@ -105,7 +110,9 @@ def divide_trainset(
             "num_samples": [len(list_for_user1), len(list_for_user2), ...]
         }
 
-    Example:
+    Example::
+    
+    >>> \\
         dataset = divide_trainset(
         trainset_path="/COCO/train2017.txt",
         num_local_class=3,
@@ -114,11 +121,11 @@ def divide_trainset(
         max_data=20,
         mode="disjoint",   # or "overlap"
         seed=42
-    )
+        )
 
-    print(dataset["users"])            # ['c_00001', ..., 'c_00005']
-    print(dataset["num_samples"])      # e.g. [10, 12, 18, 9, 15]
-    print(dataset["user_data"]["c_00001"]["filename"][:3])
+    >>> print(dataset["users"])            # ['c_00001', ..., 'c_00005']
+    >>> print(dataset["num_samples"])      # e.g. [10, 12, 18, 9, 15]
+    >>> print(dataset["user_data"]["c_00001"]["filename"][:3])
     """
     if seed is not None:
         random.seed(seed)
@@ -247,8 +254,11 @@ def init_model(model_name, num_classes) -> YOLO:
     """
     Initialize the model for a specific learning task
     Args:
-        :param model_name: Name of the model
-        :param num_classes: Number of classes
+        model_name: Name of the model
+        num_classes: Number of classes
+
+    Returns:
+        model: YOLO model instance
     """
     model = None
     if model_name == "yolo_v11_n":
@@ -273,11 +283,13 @@ def build_valset_if_available(cfg, params, args=None, val_name: str = "val2017")
     - If cfg['val_txt'] exists, use it.
     - Else if <dataset_path>/val.txt exists, use it.
     - Else return None (testing will be skipped).
+
     Args:
         cfg: config dict
         params: params dict for Dataset
         args: optional args object (for input_size)
         val_name: name of the validation set folder with no prefix (default: "val2017")
+
     Returns:
         Dataset or None
     """
@@ -344,3 +356,23 @@ def plot_curves(save_dir, hist, savename="fed_yolo_curves.png"):
     out_png = os.path.join(save_dir, savename)
     plt.savefig(out_png, dpi=150, bbox_inches="tight")
     print(f"[plot] saved: {out_png}")
+
+
+def prepare_result_dir(base_root: str = "results"):
+    """
+    Prepare result directories for saving outputs.
+
+    Args:
+        base_root (str): base directory for results.
+
+    Returns:
+        (res_dir, weights_dir) (str,str): Path to result directory and weights directory.
+    """
+    os.makedirs(base_root, exist_ok=True)
+    timestamp = time.strftime("%Y%m%d_%H%M%S")
+    res_dir = os.path.join(base_root, f"result_{timestamp}")
+    weights_dir = os.path.join(res_dir, f"weight_{timestamp}")
+    os.makedirs(res_dir, exist_ok=True)
+    os.makedirs(weights_dir, exist_ok=True)
+    print(f"[INFO] Saving results to: {res_dir}")
+    return res_dir, weights_dir