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All Projects → AIOpenData → baai-federated-learning-crane-baseline

AIOpenData / baai-federated-learning-crane-baseline

Licence: Apache-2.0 License
电力人工智能数据竞赛——液压吊车目标检测赛道

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电力人工智能数据竞赛—液压吊车目标检测赛道

以下为电力人工智能数据竞赛—液压吊车目标检测赛道基准模型介绍。其中包含了智源联邦学习框架的简化版本（真实版本后期会以论文的形式发布）、智源整理标注的初赛吊车数据集和基于YOLOv3模型完成的实验。

环境要求

  • Python==3.7.6
  • Flask==1.1.1
  • gevent==20.9.0
  • loguru==0.5.3
  • numpy==1.18.1
  • Pillow==7.0.0
  • torch==1.6.0
  • terminaltables==3.1.0
  • torchvision==0.7.0
  • tqdm==4.42.1

详情请参考baai-federated-learning-crane-baseline下面的requirements.txt

项目结构

.
├── README.md
├── baai-client  # 智源联邦学习客户端
│   ├── api
│   │   ├── __init__.py
│   │   └── my_api.py  # 选手联邦学习服务端需要调用智源联邦学习客户端的函数（训练、测试等）
│   ├── config
│   │   ├── __init__.py
│   │   └── project_conf.py  # 本机端口号和环境配置
│   ├── log
│   │   ├── D_preliminary_contest_crane_federal_M_yolov3_SE_25_CE_4_ID_1.json  # 最后测试集提交结果
│   │   └── D_preliminary_contest_crane_federal_M_yolov3_SE_25_CE_4_ID_1.log  # 训练、测试过程完整日志
│   ├── main.py  # 智源联邦学习客户端启动主函数
│   ├── service
│   │   └── federated
│   │       ├── client.py  # 智源联邦学习客户端类
│   │       ├── config
│   │       │   └── preliminary_contest_crane_federal
│   │       │       ├── create_preliminary_contest_crane_federal_model.sh  # 构建yolov3模型的bash脚本
│   │       │       ├── preliminary_contest_crane_federal.data  # 本地初赛吊车数据集路径信息
│   │       │       └── yolov3_preliminary_contest_crane_federal.cfg  # 根据bash脚本生成的cfg文件
│   │       ├── models
│   │       │   └── models.py  # yolov3模型相关类
│   │       ├── utils
│   │       │   ├── data.py  # 处理加载数据集相关函数
│   │       │   ├── options.py  # 初始化参数函数
│   │       │   └── utils.py  # 辅助功能函数
│   └── utils
│       ├── __init__.py
│       ├── common_utils.py  # 常用功能函数
│       ├── http_request_utils.py  # 通信功能函数（GET、POST）
│       ├── request_api.py  # 调用通信功能函数api
│       └── result_utils.py  # 通信结果封装类
├── contestant-server  # 选手联邦学习服务端
│   ├── config
│   │   ├── __init__.py
│   │   └── project_conf.py  # 本机端口号和环境配置
│   ├── log
│   │   └── D_preliminary_contest_crane_federal_M_yolov3_SE_25_CE_4.log  # 训练、测试过程完整日志
│   ├── service
│   │   └── federated
│   │       ├── config
│   │       │   └── preliminary_contest_crane_federal
│   │       │       ├── create_preliminary_contest_crane_federal_model.sh  # 构建yolov3模型的bash脚本
│   │       │       └── yolov3_preliminary_contest_crane_federal.cfg  # 根据bash脚本生成的cfg文件
│   │       ├── models
│   │       │   └── models.py  # yolov3模型相关类
│   │       ├── server.py
│   │       ├── utils
│   │       │   ├── options.py  # 初始化参数函数
│   │       │   └── utils.py  # 辅助功能函数
│   │       └── weights  # 保存yolov3预训练模型
│   └── utils
│       ├── __init__.py
│       ├── common_utils.py  # 常用功能函数
│       ├── http_request_utils.py  # 通信功能函数（GET、POST）
│       ├── request_api.py  # 调用通信功能函数api
│       └── result_utils.py  # 通信结果封装类
├── requirements.txt  # 需要安装的python库
└── sgcc-client  # 国网电力联邦学习客户端（功能与智源联邦学习客户端相同，此处不展开介绍）
    ├── api
    │   ├── __init__.py
    │   └── my_api.py
    ├── config
    │   ├── __init__.py
    │   └── project_conf.py
    ├── log
    │   ├── D_preliminary_contest_crane_federal_M_yolov3_SE_25_CE_4_ID_2.json
    │   └── D_preliminary_contest_crane_federal_M_yolov3_SE_25_CE_4_ID_2.log
    ├── main.py
    ├── service
    │   └── federated
    │       ├── client.py
    │       ├── config
    │       │   └── preliminary_contest_crane_federal
    │       │       ├── create_preliminary_contest_crane_federal_model.sh
    │       │       ├── preliminary_contest_crane_federal.data
    │       │       └── yolov3_preliminary_contest_crane_federal.cfg
    │       ├── models
    │       │   └── models.py
    │       ├── utils
    │       │   ├── data.py
    │       │   ├── options.py
    │       │   └── utils.py
    └── utils
        ├── __init__.py
        ├── common_utils.py
        ├── http_request_utils.py
        ├── request_api.py
        └── result_utils.py

下载地址

  • 初赛吊车数据集
    • 其中智源客户端的数据集包括
      • preliminary_contest_crane_federal/annotations下面的/train1.json，val1.json，test.json，test_image_info.json
      • preliminary_contest_crane_federal/images下面的/train1，val1，test
    • 国网电力客户端的数据集包括
      • preliminary_contest_crane_federal/annotations下面的train2.json，val2.json，test.json，test_image_info.json
      • preliminary_contest_crane_federal/images下面的train2，val2，test；
  • yolov3预训练模型
    当前主要采用weights下面的darknet53.conv.74

运行方式

智源联邦学习客户端

  • 进入baai-client/service/federated/config/preliminary_contest_crane_federal目录

    • 修改preliminary_contest_crane_federal.data当中的数据路径
    • 生成yolov3模型的cfg文件（先删除旧的cfg文件）
      bash create_preliminary_contest_crane_federal_model.sh 1

  • 进入baai-client/config

    • 修改project_conf.py当中的host和port

  • 进入baai-client/service/federated/utils目录

    • 配置参数，特别是data_config，model_def，server_ip，server_port，client_ip，client_port

  • 启动智源联邦学习客户端

    • 进入baai-client，运行以下指令
      python main.py

国网电力联邦学习客户端

  • 进入sgcc-client/service/federated/config/preliminary_contest_crane_federal目录

    • 修改preliminary_contest_crane_federal.data当中的数据路径
    • 生成yolov3模型的cfg文件（先删除旧的cfg文件）
      bash create_preliminary_contest_crane_federal_model.sh 1

  • 进入sgcc-client/config

    • 修改project_conf.py当中的host和port

  • 进入sgcc-client/service/federated/utils目录

    • 配置参数，特别是data_config，model_def，server_ip，server_port，client_ip，client_port

  • 启动国网电力联邦学习客户端

    • 进入sgcc-client，运行以下指令
      python main.py

选手联邦学习服务端

  • 把下载好的yolov3预训练模型拷贝到contestant-server/service/federated/weights

  • 进入contestant-server/service/federated/config/preliminary_contest_crane_federal目录

    • 生成yolov3模型的cfg文件（先删除旧的cfg文件）
      bash create_preliminary_contest_crane_federal_model.sh 1

  • 进入contestant-server/config

    • 修改project_conf.py当中的host和port

  • 进入contestant-server/service/federated/utils目录

    • 配置参数，特别是pretrained_weights，model_def，server_ip，server_port，client_ips，client_ports

  • 启动选手联邦学习服务端

    • 进入contestant-server，运行以下指令
      PYTHONPATH=your/project/path/contestant-server python service/federated/server.py

实验指标

  • 正确率：P (Precision) = TP / (TP + FP)，所有预测出来的正例中有多少是真的正例
  • 召回率：R (Recall) = TP / (TP + FN)，所有真实正例中预测出了多少真实正例
  • F1值：F1 Score = 2 * P * R / (P + R)，精确率和召回率的调和均值
  • mAP (mean Average Precision): 目标检测模型的评估指标
  • 国网电力指标：电力人工智能数据竞赛指标计算方法和自动评测脚本的详细介绍

实验结果

基于默认实验参数，初赛吊车测试集基于YOLOv3模型的结果：

false detection rate missed detection rate object detection correct rate sgcc crane image score
0.5656324582338902 0.10780669144981413 0.7336343115124153 0.723133779107409

选手问题答疑

问题1：

File "your/python/path/python3.7/site-packages/torch/nn/modules/container.py", line 74, in _get_item_by_idx  
raise IndexError('Index {} is out of range'.format(idx))  
IndexError: index 0 is out of range

解决方案：在客户端和服务端生成新的cfg文件之前，先把旧的cfg文件删除掉。

问题2：

File "service/federated/server.py", line 81, in call_federated_train_size  
federated_train_size = Common.get_dict_by_json_str_func(train_job.value['data'])["federated_train_size"]  
TypeError: 'NoneType' object is not subscriptable

解决方案：查看客户端报错信息，如果是Flask包相关错误，将Flask升级为1.1.1版本。

问题3：

Start executing step: call federated train size  
Segmentation fault (core dumped)

解决方案：很可能是gevent版本的问题，升级到20.9.0版本。

问题4：
电力指标相关的问题。
已经在官网更新国网电力指标说明。

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