增加联邦学习指标；fix：Pytorch 加载模型不匹配
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@@ -1,6 +1,7 @@
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import glob
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import os
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from pathlib import Path
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import json
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import yaml
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from ultralytics import YOLO
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@@ -31,105 +32,170 @@ def federated_avg(global_model, client_weights):
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# 加权平均
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if global_dict[key].dtype == torch.float32: # 只聚合浮点型参数
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# 跳过 BatchNorm 层的统计量
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if any(x in key for x in ['running_mean', 'running_var', 'num_batches_tracked']):
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if any(
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x in key for x in ["running_mean", "running_var", "num_batches_tracked"]
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):
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continue
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# 按照样本数加权求和
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weighted_tensors = [sd[key].float() * (n / total_samples)
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for sd, n in zip(state_dicts, sample_counts)]
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weighted_tensors = [
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sd[key].float() * (n / total_samples)
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for sd, n in zip(state_dicts, sample_counts)
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]
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global_dict[key] = torch.stack(weighted_tensors, dim=0).sum(dim=0)
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# 解决模型参数不匹配问题
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try:
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# try:
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# # 加载回YOLO模型
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# global_model.model.load_state_dict(global_dict)
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# except RuntimeError as e:
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# print('Ignoring "' + str(e) + '"')
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# 加载回YOLO模型
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global_model.model.load_state_dict(global_dict)
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except RuntimeError as e:
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print('Ignoring "' + str(e) + '"')
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# 添加调试输出
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print("\n=== 参数聚合检查 ===")
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# 选取一个典型参数层
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# sample_key = list(global_dict.keys())[10]
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# original = global_dict[sample_key].data.mean().item()
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# aggregated = torch.stack([w[sample_key] for w in client_weights]).mean().item()
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# print(f"参数层 '{sample_key}' 变化: {original:.4f} → {aggregated:.4f}")
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# print(f"客户端参数差异: {[w[sample_key].mean().item() for w in client_weights]}")
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# 随机选取一个非统计量层进行对比
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sample_key = next(k for k in global_dict if 'running_' not in k)
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aggregated_mean = global_dict[sample_key].mean().item()
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client_means = [sd[sample_key].float().mean().item() for sd in state_dicts]
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print(f"layer: '{sample_key}' Mean after aggregation: {aggregated_mean:.6f}")
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print(f"The average value of the layer for each client: {client_means}")
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# sample_key = next(k for k in global_dict if 'running_' not in k)
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# aggregated_mean = global_dict[sample_key].mean().item()
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# client_means = [sd[sample_key].float().mean().item() for sd in state_dicts]
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# print(f"layer: '{sample_key}' Mean after aggregation: {aggregated_mean:.6f}")
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# print(f"The average value of the layer for each client: {client_means}")
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# 定义多个关键层
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MONITOR_KEYS = [
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"model.0.conv.weight", # 输入层卷积
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"model.10.conv.weight", # 中间层卷积
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"model.22.dfl.conv.weight", # 输出层分类头
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]
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with open("aggregation_check.txt", "a") as f:
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f.write("\n=== 参数聚合检查 ===\n")
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for key in MONITOR_KEYS:
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if key not in global_dict:
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continue
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# 计算聚合后均值
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aggregated_mean = global_dict[key].mean().item()
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# 计算各客户端均值
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client_means = [sd[key].float().mean().item() for sd in state_dicts]
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with open("aggregation_check.txt", "a") as f:
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f.write(f"层 '{key}' 聚合后均值: {aggregated_mean:.6f}\n")
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f.write(f"各客户端该层均值差异: {[f'{cm:.6f}' for cm in client_means]}\n")
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f.write(f"客户端最大差异: {max(client_means) - min(client_means):.6f}\n\n")
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return global_model
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# ------------ 修改训练流程 ------------
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def federated_train(num_rounds, clients_data):
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# ========== 新增：初始化指标记录 ==========
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metrics = {
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"round": [],
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"val_mAP": [], # 每轮验证集mAP
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"train_loss": [], # 每轮平均训练损失
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"client_mAPs": [], # 各客户端本地模型在验证集上的mAP
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"communication_cost": [], # 每轮通信开销（MB）
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}
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# 初始化全局模型
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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global_model = YOLO("../yolov8n.pt").to(device)
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global_model = YOLO("../yolov8n.yaml").to(device)
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# 设置类别数
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global_model.model.nc = 1
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# global_model.model.nc = 1
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for _ in range(num_rounds):
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client_weights = []
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client_losses = [] # 记录各客户端的训练损失
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# 每个客户端本地训练
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for data_path in clients_data:
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# 统计本地训练样本数
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with open(data_path, 'r') as f:
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with open(data_path, "r") as f:
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config = yaml.safe_load(f)
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# Resolve img_dir relative to the YAML file's location
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yaml_dir = os.path.dirname(data_path)
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img_dir = os.path.join(yaml_dir, config.get('train', data_path)) # 从配置文件中获取图像目录
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img_dir = os.path.join(
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yaml_dir, config.get("train", data_path)
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) # 从配置文件中获取图像目录
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# print(f"Image directory: {img_dir}")
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num_samples = (len(glob.glob(os.path.join(img_dir, '*.jpg'))) +
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len(glob.glob(os.path.join(img_dir, '*.png'))))
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num_samples = (len(glob.glob(os.path.join(img_dir, "*.jpg")))
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+ len(glob.glob(os.path.join(img_dir, "*.png")))
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+ len(glob.glob(os.path.join(img_dir, "*.jpeg")))
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)
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# print(f"Number of images: {num_samples}")
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# 克隆全局模型
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local_model = copy.deepcopy(global_model)
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# 本地训练（保持你的原有参数设置）
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local_model.train(
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results = local_model.train(
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data=data_path,
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epochs=16, # 每轮本地训练1个epoch
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save_period=16,
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epochs=4, # 每轮本地训练多少个epoch
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# save_period=16,
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imgsz=640, # 图像大小
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verbose=False, # 关闭冗余输出
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batch=-1
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batch=-1,
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)
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# 记录客户端训练损失
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# client_loss = results.results_dict['train_loss']
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# client_losses.append(client_loss)
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# 收集模型参数及样本数
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client_weights.append((copy.deepcopy(local_model.model.state_dict()), num_samples))
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client_weights.append(
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(copy.deepcopy(local_model.model.state_dict()), num_samples)
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)
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# 聚合参数更新全局模型
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global_model = federated_avg(global_model, client_weights)
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print(f"Round {_ + 1}/{num_rounds} completed.")
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return global_model
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# ========== 评估全局模型 ==========
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# 评估全局模型在验证集上的性能
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val_results = global_model.val(
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data="/mnt/DATA/UAVdataset/data.yaml", # 指定验证集配置文件
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imgsz=640,
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batch=-1,
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verbose=False,
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)
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val_mAP = val_results.box.map # 获取mAP@0.5
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# 计算平均训练损失
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# avg_train_loss = sum(client_losses) / len(client_losses)
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# 计算通信开销（假设传输全部模型参数）
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model_size = sum(p.numel() * 4 for p in global_model.model.parameters()) / (
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1024**2
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) # MB
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# 记录到指标容器
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metrics["round"].append(_ + 1)
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metrics["val_mAP"].append(val_mAP)
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# metrics['train_loss'].append(avg_train_loss)
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metrics["communication_cost"].append(model_size)
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# 打印当前轮次结果
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with open("aggregation_check.txt", "a") as f:
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f.write(f"\n[Round {_ + 1}/{num_rounds}]")
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f.write(f"Validation mAP@0.5: {val_mAP:.4f}")
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# f.write(f"Average Train Loss: {avg_train_loss:.4f}")
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f.write(f"Communication Cost: {model_size:.2f} MB\n")
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return global_model, metrics
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# ------------ 使用示例 ------------
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if __name__ == "__main__":
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# 联邦训练配置
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clients_config = [
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"/root/autodl-tmp/dataset/train1/train1.yaml", # 客户端1数据路径
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"/root/autodl-tmp/dataset/train2/train2.yaml" # 客户端2数据路径
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"/mnt/DATA/uav_dataset_fed/train1/train1.yaml", # 客户端1数据路径
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"/mnt/DATA/uav_dataset_fed/train2/train2.yaml", # 客户端2数据路径
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]
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# 运行联邦训练
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final_model = federated_train(num_rounds=10, clients_data=clients_config)
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final_model, metrics = federated_train(num_rounds=40, clients_data=clients_config)
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# 保存最终模型
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final_model.save("yolov8n_federated.pt")
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# final_model.export(format="onnx") # 导出为ONNX格式
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# 检查1：确认模型保存
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# assert Path("yolov8n_federated.onnx").exists(), "模型导出失败"
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# 检查2：验证预测功能
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# results = final_model.predict("../dataset/val/images/VS_P65.jpg", save=True)
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# assert len(results[0].boxes) > 0, "预测结果异常"
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with open("metrics.json", "w") as f:
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json.dump(metrics, f, indent=4)
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49
yolov8.yaml
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49
yolov8.yaml
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@@ -0,0 +1,49 @@
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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# Ultralytics YOLOv8 object detection model with P3/8 - P5/32 outputs
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# Model docs: https://docs.ultralytics.com/models/yolov8
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# Task docs: https://docs.ultralytics.com/tasks/detect
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# Parameters
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nc: 1 # number of classes
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scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
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# [depth, width, max_channels]
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n: [0.33, 0.25, 1024] # YOLOv8n summary: 129 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPS
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s: [0.33, 0.50, 1024] # YOLOv8s summary: 129 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPS
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m: [0.67, 0.75, 768] # YOLOv8m summary: 169 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPS
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l: [1.00, 1.00, 512] # YOLOv8l summary: 209 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPS
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x: [1.00, 1.25, 512] # YOLOv8x summary: 209 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPS
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# YOLOv8.0n backbone
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backbone:
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# [from, repeats, module, args]
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- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
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- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
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- [-1, 3, C2f, [128, True]]
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- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
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- [-1, 6, C2f, [256, True]]
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- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
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- [-1, 6, C2f, [512, True]]
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- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
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- [-1, 3, C2f, [1024, True]]
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- [-1, 1, SPPF, [1024, 5]] # 9
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# YOLOv8.0n head
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head:
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- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
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- [[-1, 6], 1, Concat, [1]] # cat backbone P4
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- [-1, 3, C2f, [512]] # 12
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- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
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- [[-1, 4], 1, Concat, [1]] # cat backbone P3
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- [-1, 3, C2f, [256]] # 15 (P3/8-small)
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- [-1, 1, Conv, [256, 3, 2]]
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- [[-1, 12], 1, Concat, [1]] # cat head P4
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- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
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- [-1, 1, Conv, [512, 3, 2]]
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- [[-1, 9], 1, Concat, [1]] # cat head P5
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- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
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- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)
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