基于AI Studio和PaddleDetection的目标检测模型训练

企业开发 2023-07-22 20:35:39 阅读次数: 0

0 前言

目标检测是一种典型的计算机视觉任务，指的是对一张图像中感兴趣的区域进行位置标定和类别识别，有时还要给出所标定区域的置信度。本文将以货车局部检测为例进行讲解，如下图所示。
在这里插入图片描述

1 AI Studio简介

AI Studio 是基于百度深度学习开源平台飞桨的人工智能学习与实训社区，为开发者提供了功能强大的线上训练环境、免费GPU算力及存储资源。本次训练使用的环境配置如下（如果是使用自己的GPU服务器环境搭建可以参考：https://blog.csdn.net/loutengyuan/article/details/126527326）：

  • Python版本：python 3.7
  • 框架版本：PaddlePaddle 2.2.2
  • Notebook版本：AI Studio经典版
  • PaddleDetection版本：PaddleDetection 2.3
    在这里插入图片描述

2 数据集准备

这里使用到的数据制作过程可参考：https://blog.csdn.net/loutengyuan/article/details/129751419，数据包含Annotations（存放标注xml文件）和JPEGImages（存放原始图片文件）两个文件夹：

├─Annotations
│ ├── image1.xml
│ ├── image2.xml
│ ├── image3.xml
│ ├── image4.xml
│ ├── image5.xml
│ └── image6.xml
└─JPEGImages
├── image1.jpg
├── image2.jpg
├── image3.jpg
├── image4.jpg
├── image5.jpg
└── image6.jpg

将数据压缩，上传到AI Studio平台创建数据集，并上传该压缩文件上传到数据集中：
在这里插入图片描述
完成数据集的准备后就可以开始接下来的训练了。

3 模型训练

3.1 项目创建

在AI Studio平台安装前面给出的环境配置创建一个空项目，并挂载上一步创建的数据集：
在这里插入图片描述

3.2 项目运行

AI Studio平台每天会免费提供8个点算力，这里我选择了 高级GPU V100 32GB 配置，每小时1个点的算力，所以每天可以使用8小时（这里可以根据训练的模型选择合适的配置）。
在这里插入图片描述

3.3 训练环境准备

项目运行起来后，还需要准备一下训练的环境：

# 更换当前路径
%cd /home/aistudio/

# 下载PaddleDetection代码
!git clone https://gitee.com/paddlepaddle/PaddleDetection

# 下载依赖
!pip install -r PaddleDetection/requirements.txt

# 更换当前路径
%cd /home/aistudio/PaddleDetection
# 编译安装paddledet
!python setup.py install

#  安装paddlex:为了方便切分数据集
!pip install paddlex

#  查看gpu版本号
!cat /usr/local/cuda/version.txt

#  下载对应的版本
!python -m pip install paddlepaddle-gpu==2.2.2.post101 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html

3.4 解压数据集

将挂载的数据集压缩文件解压：

# 解压数据
!unzip -o /home/aistudio/data/data177504/truck_detect_voc_20230307.zip -d /home/aistudio/PaddleDetection/dataset/

解压后在 PaddleDetection/dataset/ 目录下就多了一个 truck_detect_voc 文件夹，里面就是之前准备的VOC格式训练数据集。
在这里插入图片描述

3.5 分割数据集

# 更换当前路径
%cd /home/aistudio/PaddleDetection/dataset/
# 切分数据集
!paddlex --split_dataset --format VOC --dataset_dir truck_detect_voc --val_value 0.2 --test_value 0.1

在这里插入图片描述
train_list.txt、val_list.txt、test_list.txt 文件格式如下：

JPEGImages/image1.jpg Annotations/image1.xml
JPEGImages/image2.jpg Annotations/image2.xml
JPEGImages/image3.jpg Annotations/image3.xml
JPEGImages/image4.jpg Annotations/image4.xml
JPEGImages/image5.jpg Annotations/image5.xml
JPEGImages/image6.jpg Annotations/image6.xml
......

3.6 修改训练配置文件

将 PaddleDetection/configs/yolov3/ 目录下的 yolov3_darknet53_270e_voc.yml 文件内容拷贝到新建的 yolov3_darknet53_270e_voc_truck_detect.yml 文件中，修改里面内容如下：

_BASE_: [
  # 指定训练数据集
  '../datasets/voc_truck_detect.yml',
  '../runtime.yml',
  '_base_/optimizer_270e.yml',
  '_base_/yolov3_darknet53.yml',
  '_base_/yolov3_reader.yml',
]

snapshot_epoch: 5
# 指定最终输出文件
weights: output/yolov3_darknet53_270e_voc_truck_detect/model_final

# set collate_batch to false because ground-truth info is needed
# on voc dataset and should not collate data in batch when batch size
# is larger than 1.
EvalReader:
  collate_batch: false

将 PaddleDetection/configs/datasets/ 目录下的 voc.yml 文件内容拷贝到新建的 voc_truck_detect.yml 文件中，修改里面内容如下：

# 指定训练数据格式为VOC
metric: VOC
map_type: 11point
# 训练数据集中目标类别数量
num_classes: 5

TrainDataset:
  !VOCDataSet
    # 指定训练数据集
    dataset_dir: /home/aistudio/PaddleDetection/dataset/truck_detect_voc
    anno_path: train_list.txt
    label_list: labels.txt
    data_fields: ['image', 'gt_bbox', 'gt_class', 'difficult']

EvalDataset:
  !VOCDataSet
    # 指定验证数据集
    dataset_dir: /home/aistudio/PaddleDetection/dataset/truck_detect_voc
    anno_path: val_list.txt
    label_list: labels.txt
    data_fields: ['image', 'gt_bbox', 'gt_class', 'difficult']

TestDataset:
  !ImageFolder
    anno_path: /home/aistudio/PaddleDetection/dataset/truck_detect_voc/labels.txt

3.7 开始训练/继续训练

执行如下命令开始或继续训练：

# 更换当前路径
%cd /home/aistudio/
#  因为训练机器是单卡GPU，所以这里设置为0，如果是多卡训练，根据实际情况设置
!export CUDA_VISIBLE_DEVICES=0 #,1,2,3
# 开始训练
!python  PaddleDetection/tools/train.py -c PaddleDetection/configs/yolov3/yolov3_darknet53_270e_voc_truck_detect.yml
# 继续训练，其中 output/yolov3_darknet53_270e_voc_truck_detect/254 表示之前训练保存的文件 
!python  PaddleDetection/tools/train.py -c PaddleDetection/configs/yolov3/yolov3_darknet53_270e_voc_truck_detect.yml -r output/yolov3_darknet53_270e_voc_truck_detect/254 --eval --amp

训练日志：

/home/aistudio
W0329 19:48:38.160742  4144 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1
W0329 19:48:38.217834  4144 device_context.cc:465] device: 0, cuDNN Version: 7.6.
[03/29 19:48:42] ppdet.utils.download INFO: Downloading DarkNet53_pretrained.pdparams from https://paddledet.bj.bcebos.com/models/pretrained/DarkNet53_pretrained.pdparams
100%|████████████████████████████████| 158704/158704 [00:15<00:00, 10205.06KB/s]
[03/29 19:48:59] ppdet.utils.checkpoint INFO: Finish loading model weights: /home/aistudio/.cache/paddle/weights/DarkNet53_pretrained.pdparams
[03/29 19:48:59] ppdet.engine INFO: Epoch: [0] [   0/1885] learning_rate: 0.000000 loss_xy: 2.979628 loss_wh: 4.050179 loss_obj: 16323.830078 loss_cls: 4.739660 loss: 16335.599609 eta: 2 days, 3:14:35 batch_cost: 0.3625 data_cost: 0.0043 ips: 22.0712 images/s
[03/29 19:49:06] ppdet.engine INFO: Epoch: [0] [  20/1885] learning_rate: 0.000005 loss_xy: 4.550855 loss_wh: 4.956159 loss_obj: 1370.894165 loss_cls: 6.475554 loss: 1390.607056 eta: 1 day, 22:53:41 batch_cost: 0.3302 data_cost: 0.1292 ips: 24.2291 images/s
[03/29 19:49:13] ppdet.engine INFO: Epoch: [0] [  40/1885] learning_rate: 0.000010 loss_xy: 4.161049 loss_wh: 4.574360 loss_obj: 17.324505 loss_cls: 6.370095 loss: 32.187752 eta: 2 days, 2:58:15 batch_cost: 0.3909 data_cost: 0.1634 ips: 20.4679 images/s
[03/29 19:49:20] ppdet.engine INFO: Epoch: [0] [  60/1885] learning_rate: 0.000015 loss_xy: 4.205465 loss_wh: 4.053814 loss_obj: 12.500505 loss_cls: 6.107187 loss: 26.668793 eta: 2 days, 2:30:44 batch_cost: 0.3507 data_cost: 0.1385 ips: 22.8106 images/s
[03/29 19:49:28] ppdet.engine INFO: Epoch: [0] [  80/1885] learning_rate: 0.000020 loss_xy: 4.379153 loss_wh: 3.988246 loss_obj: 12.806162 loss_cls: 6.259327 loss: 27.258268 eta: 2 days, 2:54:20 batch_cost: 0.3687 data_cost: 0.1606 ips: 21.6999 images/s
[03/29 19:49:35] ppdet.engine INFO: Epoch: [0] [ 100/1885] learning_rate: 0.000025 loss_xy: 4.374406 loss_wh: 3.631483 loss_obj: 12.187485 loss_cls: 5.954453 loss: 26.480440 eta: 2 days, 3:06:37 batch_cost: 0.3675 data_cost: 0.1569 ips: 21.7681 images/s
[03/29 19:49:42] ppdet.engine INFO: Epoch: [0] [ 120/1885] learning_rate: 0.000030 loss_xy: 4.085462 loss_wh: 3.361600 loss_obj: 11.558670 loss_cls: 5.272717 loss: 24.563915 eta: 2 days, 2:53:52 batch_cost: 0.3526 data_cost: 0.1381 ips: 22.6892 images/s
[03/29 19:49:50] ppdet.engine INFO: Epoch: [0] [ 140/1885] learning_rate: 0.000035 loss_xy: 3.668382 loss_wh: 2.828609 loss_obj: 10.547073 loss_cls: 4.500311 loss: 21.663486 eta: 2 days, 2:57:11 batch_cost: 0.3630 data_cost: 0.1590 ips: 22.0411 images/s
[03/29 19:49:56] ppdet.engine INFO: Epoch: [0] [ 160/1885] learning_rate: 0.000040 loss_xy: 4.295379 loss_wh: 3.293975 loss_obj: 11.571999 loss_cls: 4.956268 loss: 23.901939 eta: 2 days, 2:29:34 batch_cost: 0.3344 data_cost: 0.1394 ips: 23.9222 images/s
[03/29 19:50:04] ppdet.engine INFO: Epoch: [0] [ 180/1885] learning_rate: 0.000045 loss_xy: 4.463886 loss_wh: 3.281782 loss_obj: 11.444370 loss_cls: 4.747983 loss: 24.420515 eta: 2 days, 2:47:36 batch_cost: 0.3766 data_cost: 0.1649 ips: 21.2408 images/s
[03/29 19:50:12] ppdet.engine INFO: Epoch: [0] [ 200/1885] learning_rate: 0.000050 loss_xy: 3.771811 loss_wh: 2.779631 loss_obj: 10.306545 loss_cls: 4.216544 loss: 20.910732 eta: 2 days, 3:07:28 batch_cost: 0.3831 data_cost: 0.1933 ips: 20.8822 images/s
[03/29 19:50:18] ppdet.engine INFO: Epoch: [0] [ 220/1885] learning_rate: 0.000055 loss_xy: 3.826384 loss_wh: 2.561981 loss_obj: 10.145305 loss_cls: 3.926377 loss: 20.392998 eta: 2 days, 2:55:39 batch_cost: 0.3465 data_cost: 0.1411 ips: 23.0862 images/s
[03/29 19:50:26] ppdet.engine INFO: Epoch: [0] [ 240/1885] learning_rate: 0.000060 loss_xy: 4.362038 loss_wh: 2.609330 loss_obj: 10.681217 loss_cls: 4.222875 loss: 21.947245 eta: 2 days, 2:53:34 batch_cost: 0.3576 data_cost: 0.1561 ips: 22.3724 images/s
[03/29 19:50:33] ppdet.engine INFO: Epoch: [0] [ 260/1885] learning_rate: 0.000065 loss_xy: 4.060377 loss_wh: 2.608733 loss_obj: 9.662804 loss_cls: 3.905273 loss: 19.952328 eta: 2 days, 2:49:26 batch_cost: 0.3540 data_cost: 0.1458 ips: 22.6006 images/s
[03/29 19:50:40] ppdet.engine INFO: Epoch: [0] [ 280/1885] learning_rate: 0.000070 loss_xy: 3.299124 loss_wh: 2.124129 loss_obj: 9.032528 loss_cls: 3.533381 loss: 17.795029 eta: 2 days, 2:54:45 batch_cost: 0.3687 data_cost: 0.1702 ips: 21.6977 images/s
[03/29 19:50:48] ppdet.engine INFO: Epoch: [0] [ 300/1885] learning_rate: 0.000075 loss_xy: 3.906075 loss_wh: 2.350418 loss_obj: 9.924751 loss_cls: 3.748957 loss: 19.654987 eta: 2 days, 2:59:08 batch_cost: 0.3683 data_cost: 0.1608 ips: 21.7204 images/s
[03/29 19:50:56] ppdet.engine INFO: Epoch: [0] [ 320/1885] learning_rate: 0.000080 loss_xy: 3.889876 loss_wh: 2.479131 loss_obj: 9.141432 loss_cls: 3.764449 loss: 19.268023 eta: 2 days, 3:40:00 batch_cost: 0.4384 data_cost: 0.2334 ips: 18.2463 images/s
[03/29 19:51:03] ppdet.engine INFO: Epoch: [0] [ 340/1885] learning_rate: 0.000085 loss_xy: 3.585271 loss_wh: 2.380863 loss_obj: 8.867376 loss_cls: 3.508474 loss: 17.948868 eta: 2 days, 3:29:26 batch_cost: 0.3447 data_cost: 0.1240 ips: 23.2089 images/s
[03/29 19:51:11] ppdet.engine INFO: Epoch: [0] [ 360/1885] learning_rate: 0.000090 loss_xy: 3.408739 loss_wh: 2.129457 loss_obj: 8.074052 loss_cls: 3.069366 loss: 16.955669 eta: 2 days, 3:31:29 batch_cost: 0.3691 data_cost: 0.1395 ips: 21.6747 images/s
[03/29 19:51:19] ppdet.engine INFO: Epoch: [0] [ 380/1885] learning_rate: 0.000095 loss_xy: 3.814119 loss_wh: 2.268811 loss_obj: 9.213679 loss_cls: 3.333805 loss: 19.176319 eta: 2 days, 3:55:20 batch_cost: 0.4186 data_cost: 0.2386 ips: 19.1128 images/s
[03/29 19:51:26] ppdet.engine INFO: Epoch: [0] [ 400/1885] learning_rate: 0.000100 loss_xy: 3.640779 loss_wh: 2.035562 loss_obj: 8.272722 loss_cls: 3.377781 loss: 17.540604 eta: 2 days, 3:37:56 batch_cost: 0.3267 data_cost: 0.1148 ips: 24.4881 images/s
[03/29 19:51:34] ppdet.engine INFO: Epoch: [0] [ 420/1885] learning_rate: 0.000105 loss_xy: 3.670832 loss_wh: 2.191157 loss_obj: 8.531160 loss_cls: 3.341545 loss: 18.225866 eta: 2 days, 3:54:07 batch_cost: 0.4060 data_cost: 0.2044 ips: 19.7058 images/s
[03/29 19:51:41] ppdet.engine INFO: Epoch: [0] [ 440/1885] learning_rate: 0.000110 loss_xy: 3.946175 loss_wh: 2.116696 loss_obj: 8.605946 loss_cls: 3.084132 loss: 17.808537 eta: 2 days, 3:57:42 batch_cost: 0.3771 data_cost: 0.1704 ips: 21.2162 images/s
[03/29 19:51:49] ppdet.engine INFO: Epoch: [0] [ 460/1885] learning_rate: 0.000115 loss_xy: 4.071396 loss_wh: 1.943774 loss_obj: 8.716118 loss_cls: 3.235123 loss: 18.250977 eta: 2 days, 4:04:14 batch_cost: 0.3860 data_cost: 0.1692 ips: 20.7261 images/s
[03/29 19:51:56] ppdet.engine INFO: Epoch: [0] [ 480/1885] learning_rate: 0.000120 loss_xy: 3.293082 loss_wh: 1.645013 loss_obj: 7.791773 loss_cls: 2.824843 loss: 15.625689 eta: 2 days, 3:52:38 batch_cost: 0.3361 data_cost: 0.1366 ips: 23.8052 images/s
[03/29 19:52:03] ppdet.engine INFO: Epoch: [0] [ 500/1885] learning_rate: 0.000125 loss_xy: 3.550079 loss_wh: 1.871112 loss_obj: 8.082129 loss_cls: 2.853400 loss: 16.612953 eta: 2 days, 3:51:59 batch_cost: 0.3657 data_cost: 0.1631 ips: 21.8731 images/s

训练过程中产生的模型文件和最终模型将存放在 output/yolov3_darknet53_270e_voc_truck_detect/ 目录下：
在这里插入图片描述

3.8 验证模型

# 更换当前路径
%cd /home/aistudio/
# 验证中间模型
# !python PaddleDetection/tools/eval.py -c PaddleDetection/configs/yolov3/yolov3_darknet53_270e_voc_truck_detect.yml -o weights=output/yolov3_darknet53_270e_voc_truck_detect/9
# 验证最佳模型
# !python PaddleDetection/tools/eval.py -c PaddleDetection/configs/yolov3/yolov3_darknet53_270e_voc_truck_detect.yml -o weights=output/yolov3_darknet53_270e_voc_truck_detect/best_model
# 验证最终模型
!python PaddleDetection/tools/eval.py -c PaddleDetection/configs/yolov3/yolov3_darknet53_270e_voc_truck_detect.yml -o weights=output/yolov3_darknet53_270e_voc_truck_detect/model_final

验证模型日志：

/home/aistudio
W0329 20:00:02.410827  5323 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1
W0329 20:00:02.416776  5323 device_context.cc:465] device: 0, cuDNN Version: 7.6.
[03/29 20:00:07] ppdet.utils.checkpoint INFO: Finish loading model weights: output/yolov3_darknet53_270e_voc_truck_detect/model_final.pdparams
[03/29 20:00:07] ppdet.engine INFO: Eval iter: 0
[03/29 20:00:10] ppdet.engine INFO: Eval iter: 100
[03/29 20:00:13] ppdet.engine INFO: Eval iter: 200
[03/29 20:00:15] ppdet.engine INFO: Eval iter: 300
[03/29 20:00:18] ppdet.engine INFO: Eval iter: 400
[03/29 20:00:21] ppdet.engine INFO: Eval iter: 500
[03/29 20:00:23] ppdet.engine INFO: Eval iter: 600
[03/29 20:00:26] ppdet.engine INFO: Eval iter: 700
[03/29 20:00:29] ppdet.engine INFO: Eval iter: 800
[03/29 20:00:32] ppdet.engine INFO: Eval iter: 900
[03/29 20:00:35] ppdet.engine INFO: Eval iter: 1000
[03/29 20:00:37] ppdet.engine INFO: Eval iter: 1100
[03/29 20:00:40] ppdet.engine INFO: Eval iter: 1200
[03/29 20:00:43] ppdet.engine INFO: Eval iter: 1300
[03/29 20:00:46] ppdet.engine INFO: Eval iter: 1400
[03/29 20:00:48] ppdet.engine INFO: Eval iter: 1500
[03/29 20:00:52] ppdet.engine INFO: Eval iter: 1600
[03/29 20:00:54] ppdet.engine INFO: Eval iter: 1700
[03/29 20:00:57] ppdet.engine INFO: Eval iter: 1800
[03/29 20:01:00] ppdet.engine INFO: Eval iter: 1900
[03/29 20:01:02] ppdet.engine INFO: Eval iter: 2000
[03/29 20:01:05] ppdet.engine INFO: Eval iter: 2100
[03/29 20:01:07] ppdet.engine INFO: Eval iter: 2200
[03/29 20:01:10] ppdet.engine INFO: Eval iter: 2300
[03/29 20:01:13] ppdet.engine INFO: Eval iter: 2400
[03/29 20:01:15] ppdet.engine INFO: Eval iter: 2500
[03/29 20:01:18] ppdet.engine INFO: Eval iter: 2600
[03/29 20:01:20] ppdet.engine INFO: Eval iter: 2700
[03/29 20:01:23] ppdet.engine INFO: Eval iter: 2800
[03/29 20:01:25] ppdet.engine INFO: Eval iter: 2900
[03/29 20:01:28] ppdet.engine INFO: Eval iter: 3000
[03/29 20:01:31] ppdet.engine INFO: Eval iter: 3100
[03/29 20:01:33] ppdet.engine INFO: Eval iter: 3200
[03/29 20:01:36] ppdet.engine INFO: Eval iter: 3300
[03/29 20:01:39] ppdet.engine INFO: Eval iter: 3400
[03/29 20:01:41] ppdet.engine INFO: Eval iter: 3500
[03/29 20:01:44] ppdet.engine INFO: Eval iter: 3600
[03/29 20:01:47] ppdet.engine INFO: Eval iter: 3700
[03/29 20:01:50] ppdet.engine INFO: Eval iter: 3800
[03/29 20:01:53] ppdet.engine INFO: Eval iter: 3900
[03/29 20:01:55] ppdet.engine INFO: Eval iter: 4000
[03/29 20:01:58] ppdet.engine INFO: Eval iter: 4100
[03/29 20:02:01] ppdet.engine INFO: Eval iter: 4200
[03/29 20:02:03] ppdet.engine INFO: Eval iter: 4300
[03/29 20:02:04] ppdet.metrics.metrics INFO: Accumulating evaluatation results...
[03/29 20:02:04] ppdet.metrics.metrics INFO: mAP(0.50, 11point) = 87.00%
[03/29 20:02:04] ppdet.engine INFO: Total sample number: 4310, averge FPS: 36.84345243569448

3.9 导出模型

# 更换当前路径
%cd /home/aistudio/
#  模型导出
# !python PaddleDetection/tools/export_model.py -c PaddleDetection/configs/yolov3/yolov3_darknet53_270e_voc_truck_detect.yml --output_dir=./inference_model_truck_detect -o weights=output/yolov3_darknet53_270e_voc_truck_detect/best_model.pdparams
!python PaddleDetection/tools/export_model.py -c PaddleDetection/configs/yolov3/yolov3_darknet53_270e_voc_truck_detect.yml --output_dir=./inference_model_truck_detect -o weights=output/yolov3_darknet53_270e_voc_truck_detect/model_final.pdparams

导出模型日志：

[03/14 11:41:15] ppdet.utils.checkpoint INFO: Finish loading model weights: output/yolov3_darknet53_270e_voc_truck_detect/model_final.pdparams
[03/14 11:41:15] ppdet.engine INFO: Export inference config file to ./inference_model_truck_detect/yolov3_darknet53_270e_voc_truck_detect/infer_cfg.yml
[03/14 11:41:22] ppdet.engine INFO: Export model and saved in ./inference_model_truck_detect/yolov3_darknet53_270e_voc_truck_detect

导出后模型存放在 /home/aistudio/inference_model_truck_detect/yolov3_darknet53_270e_voc_truck_detect 目录下：
在这里插入图片描述

3.10 图片预测

# 更换当前路径
%cd /home/aistudio/
#  图片预测
!python PaddleDetection/deploy/python/infer.py --model_dir=inference_model_truck_detect/yolov3_darknet53_270e_voc_truck_detect --image_file=mydata/11111.jpg --device=GPU

预测结果如下：
在这里插入图片描述

3.11 视频预测

# 更换当前路径
%cd /home/aistudio/
#  视频预测
!python PaddleDetection/deploy/python/infer.py --model_dir=inference_model_truck_detect/yolov3_darknet53_270e_voc_truck_detect --video_file=mydata/uuuuuu.mp4 --device=GPU

预测结果如下：
在这里插入图片描述

3.12 压缩模型

# 更换当前路径
%cd /home/aistudio/
# 压缩模型文件
!zip -r -q -o inference_model_truck_detect_20230314_v1.9.zip inference_model_truck_detect/

导出的模型就可以参考：https://blog.csdn.net/loutengyuan/article/details/126532324 这篇文件用于部署了

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