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修改参数，符合Linux路径要求

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This commit is contained in:
myh 2025-04-22 14:56:45 +08:00
parent 9f827af58e
commit 69482e6a3f
3 changed files with 15 additions and 12 deletions
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2
dataset/train1/train1.yaml
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@ -1,4 +1,4 @@
train: images train: ./images
val: ../val val: ../val
nc: 1 nc: 1
names: ['uav'] names: ['uav']

2
dataset/train2/train2.yaml
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@ -1,4 +1,4 @@
train: images train: ./images
val: ../val val: ../val
nc: 1 nc: 1
names: ['uav'] names: ['uav']

23
federated_learning/yolov8_fed.py
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@ -59,8 +59,8 @@ def federated_avg(global_model, client_weights):
sample_key = next(k for k in global_dict if 'running_' not in k) sample_key = next(k for k in global_dict if 'running_' not in k)
aggregated_mean = global_dict[sample_key].mean().item() aggregated_mean = global_dict[sample_key].mean().item()
client_means = [sd[sample_key].float().mean().item() for sd in state_dicts] client_means = [sd[sample_key].float().mean().item() for sd in state_dicts]
print(f"层 '{sample_key}' 聚合后均值: {aggregated_mean:.6f}") print(f"layer: '{sample_key}' Mean after aggregation: {aggregated_mean:.6f}")
print(f"各客户端该层均值: {client_means}") print(f"The average value of the layer for each client: {client_means}")
return global_model return global_model
@ -85,10 +85,10 @@ def federated_train(num_rounds, clients_data):
yaml_dir = os.path.dirname(data_path) yaml_dir = os.path.dirname(data_path)
img_dir = os.path.join(yaml_dir, config.get('train', data_path)) # 从配置文件中获取图像目录 img_dir = os.path.join(yaml_dir, config.get('train', data_path)) # 从配置文件中获取图像目录
print(f"Image directory: {img_dir}") # print(f"Image directory: {img_dir}")
num_samples = (len(glob.glob(os.path.join(img_dir, '*.jpg'))) + num_samples = (len(glob.glob(os.path.join(img_dir, '*.jpg'))) +
len(glob.glob(os.path.join(img_dir, '*.png')))) len(glob.glob(os.path.join(img_dir, '*.png'))))
print(f"Number of images: {num_samples}") # print(f"Number of images: {num_samples}")
# 克隆全局模型 # 克隆全局模型
local_model = copy.deepcopy(global_model) local_model = copy.deepcopy(global_model)
@ -96,9 +96,11 @@ def federated_train(num_rounds, clients_data):
# 本地训练（保持你的原有参数设置） # 本地训练（保持你的原有参数设置）
local_model.train( local_model.train(
data=data_path, data=data_path,
epochs=1, # 每轮本地训练1个epoch epochs=16, # 每轮本地训练1个epoch
save_period=16,
imgsz=640, # 图像大小 imgsz=640, # 图像大小
verbose=False # 关闭冗余输出 verbose=False, # 关闭冗余输出
batch=-1
) )
# 收集模型参数及样本数 # 收集模型参数及样本数
@ -106,7 +108,7 @@ def federated_train(num_rounds, clients_data):
# 聚合参数更新全局模型 # 聚合参数更新全局模型
global_model = federated_avg(global_model, client_weights) global_model = federated_avg(global_model, client_weights)
print(f"Round {_ + 1}/{num_rounds} completed.")
return global_model return global_model
@ -114,14 +116,15 @@ def federated_train(num_rounds, clients_data):
if __name__ == "__main__": if __name__ == "__main__":
# 联邦训练配置 # 联邦训练配置
clients_config = [ clients_config = [
"../dataset/train1/train1.yaml", # 客户端1数据路径 "/root/autodl-tmp/dataset/train1/train1.yaml", # 客户端1数据路径
"../dataset/train2/train2.yaml" # 客户端2数据路径 "/root/autodl-tmp/dataset/train2/train2.yaml" # 客户端2数据路径
] ]
# 运行联邦训练 # 运行联邦训练
final_model = federated_train(num_rounds=1, clients_data=clients_config) final_model = federated_train(num_rounds=10, clients_data=clients_config)
# 保存最终模型 # 保存最终模型
final_model.save("yolov8n_federated.pt")
# final_model.export(format="onnx") # 导出为ONNX格式 # final_model.export(format="onnx") # 导出为ONNX格式
# 检查1：确认模型保存 # 检查1：确认模型保存