电梯内电瓶车检测全流程_副本
训练到部署全流程
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1.项目说明
转载自AI Studio
项目链接https://aistudio.baidu.com/aistudio/projectdetail/3497217?contributionType=1
近年来，电瓶车进楼入户发生的火灾事故屡见不鲜，针对该现象推出了相应的电瓶车入室检测模型，旨在从源头减少这一情况的发生。 针对室内摩托车模型可能会发生的误报情况，采用了额外的图像检索方式实现更为精确的识别。 本案例使用了飞桨目标检测套件PaddleDetection中的picodet模型以及图像识别套件PaddleClas中的轻量级通用识别模型。
2.数据集准备
本案例中picodet的模型数据集为VOC格式(使用labelimg制成)，包括21903张电梯中的图片，其中训练集17522张，测试集4381张，皆来自日常的电梯场景中，共有14715个摩托车的框，23058个人的框，3750个自行车的框，由于picodet使用的是coco格式，所以需要将VOC格式转换成coco格式。
生成VOC数据集：使用python的labelimg图像标注工具为原始图片生成对应的标注xml文件作为原始的VOC格式数据集，生成的xml文件格式如下图所示，其中每个object代表框出的每一个对象，object中的name表明对象的名字而bndbox中包含框的具体坐标（左上角以及右下角）。
labelimg生成的xml文件如下图所示:
生成VOC数据集：
完成图片标注后，下一步就是生成数据集，将每个图片与其xml对应起来按比例生成对应的训练集以及测试集.
%cd /home/aistudio/data/data128282
!unzip -q picodet_motorcycle.zip
/home/aistudio/data/data128282
生成的数据集文件组织结构为:
├── classify_voc.py
├── picodet_motorcycle
│ ├── Annotations
│ │ ├── 1595214506200933-1604535322-[]-motorcycle.xml
│ │ ├── 1595214506200933-1604542813-[]-motorcycle.xml
│ │ ├── 1595214506200933-1604559538-[]-motorcycle.xml
| ...
│ ├── ImageSets
│ │ └── Main
│ │ ├── test.txt
│ │ ├── train.txt
│ │ ├── trainval.txt
│ │ └── val.txt
│ └── JPEGImages
│ ├── 1595214506200933-1604535322-[]-motorcycle.jpg
│ ├── 1595214506200933-1604542813-[]-motorcycle.jpg
│ ├── 1595214506200933-1604559538-[]-motorcycle.jpg
│ | ...
├── picodet_motorcycle.zip
├── prepare_voc_data.py
├── test.txt
└── trainval.txt
训练集与测试集转换:
使用PaddleDetection自带的转换工具以及教程完成数据集的转换。
%cd /home/aistudio/work/code
!python x2coco.py --dataset_type voc --voc_anno_dir /home/aistudio/data/data128282/ --voc_anno_list /home/aistudio/data/data128282/trainval.txt --voc_label_list /home/aistudio/data/data128282/label_list.txt --voc_out_name voc_train.json
!python x2coco.py --dataset_type voc --voc_anno_dir /home/aistudio/data/data128282/ --voc_anno_list /home/aistudio/data/data128282/test.txt --voc_label_list /home/aistudio/data/data128282/label_list.txt --voc_out_name voc_test.json
%mv voc_test.json /home/aistudio/data/data128282/
%mv voc_train.json /home/aistudio/data/data128282/
/home/aistudio/work/code
Start converting !
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3.目标检测模型训练
将数据集划分为训练集以及测试集后，使用picodet_lcnet_1_5x_416_coco.yml文件便可以开始训练（该文件在PaddleDetection-release-2.3/configs/picodet/more_config/ 下有样板只需要稍微修改一下）。
预训练模型选用PicoDet_LCNet 1.5x,训练完成后可以进行评估，训练与评估的命令如下。
picodet_lcnet_1_5x_416_coco.yml 文件的内容如下所示:
使用的预训练模型如图:
#安装依赖库
%cd /home/aistudio/work/code/train/
!pip install pycocotools
!pip install faiss-gpu
!pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
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[33mWARNING: You are using pip version 21.3.1; however, version 22.0.3 is available.
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#训练模型,完成后生成在output文件夹下
%cd /home/aistudio/work/code/train/
!python3 train.py -c picodet_lcnet_1_5x_416_coco.yml -o pretrain_weights=https://paddledet.bj.bcebos.com/models/picodet_lcnet_1_5x_416_coco.pdparams --eval
/home/aistudio/work/code/train
Warning: import ppdet from source directory without installing, run 'python setup.py install' to install ppdet firstly
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
W0222 13:03:47.626899 739 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.0, Runtime API Version: 10.1
W0222 13:03:47.633911 739 device_context.cc:465] device: 0, cuDNN Version: 7.6.
[02/22 13:03:52] ppdet.utils.download INFO: Downloading picodet_lcnet_1_5x_416_coco.pdparams from https://paddledet.bj.bcebos.com/models/picodet_lcnet_1_5x_416_coco.pdparams
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[02/22 13:03:55] ppdet.utils.checkpoint INFO: The shape [112] in pretrained weight head.head_cls0.bias is unmatched with the shape [35] in model head.head_cls0.bias. And the weight head.head_cls0.bias will not be loaded
[02/22 13:03:55] ppdet.utils.checkpoint INFO: The shape [112, 128, 1, 1] in pretrained weight head.head_cls0.weight is unmatched with the shape [35, 128, 1, 1] in model head.head_cls0.weight. And the weight head.head_cls0.weight will not be loaded
[02/22 13:03:55] ppdet.utils.checkpoint INFO: The shape [112] in pretrained weight head.head_cls1.bias is unmatched with the shape [35] in model head.head_cls1.bias. And the weight head.head_cls1.bias will not be loaded
[02/22 13:03:55] ppdet.utils.checkpoint INFO: The shape [112, 128, 1, 1] in pretrained weight head.head_cls1.weight is unmatched with the shape [35, 128, 1, 1] in model head.head_cls1.weight. And the weight head.head_cls1.weight will not be loaded
[02/22 13:03:55] ppdet.utils.checkpoint INFO: The shape [112] in pretrained weight head.head_cls2.bias is unmatched with the shape [35] in model head.head_cls2.bias. And the weight head.head_cls2.bias will not be loaded
[02/22 13:03:55] ppdet.utils.checkpoint INFO: The shape [112, 128, 1, 1] in pretrained weight head.head_cls2.weight is unmatched with the shape [35, 128, 1, 1] in model head.head_cls2.weight. And the weight head.head_cls2.weight will not be loaded
[02/22 13:03:55] ppdet.utils.checkpoint INFO: The shape [112] in pretrained weight head.head_cls3.bias is unmatched with the shape [35] in model head.head_cls3.bias. And the weight head.head_cls3.bias will not be loaded
[02/22 13:03:55] ppdet.utils.checkpoint INFO: The shape [112, 128, 1, 1] in pretrained weight head.head_cls3.weight is unmatched with the shape [35, 128, 1, 1] in model head.head_cls3.weight. And the weight head.head_cls3.weight will not be loaded
[02/22 13:03:55] ppdet.utils.checkpoint INFO: Finish loading model weights: /home/aistudio/.cache/paddle/weights/picodet_lcnet_1_5x_416_coco.pdparams
[02/22 13:03:55] ppdet.engine INFO: Epoch: [0] [0/3] learning_rate: 0.010000 loss_vfl: 1.263152 loss_bbox: 1.064944 loss_dfl: 0.518176 loss: 2.846272 eta: 0:00:20 batch_cost: 0.6679 data_cost: 0.0006 ips: 29.9460 images/s
[02/22 13:04:03] ppdet.engine INFO: Epoch: [1] [0/3] learning_rate: 0.010900 loss_vfl: 1.033099 loss_bbox: 1.136453 loss_dfl: 0.515825 loss: 2.680272 eta: 0:00:49 batch_cost: 1.8156 data_cost: 1.1352 ips: 11.0156 images/s
[02/22 13:04:11] ppdet.engine INFO: Epoch: [2] [0/3] learning_rate: 0.011800 loss_vfl: 0.916189 loss_bbox: 1.148422 loss_dfl: 0.512133 loss: 2.595586 eta: 0:00:44 batch_cost: 1.8745 data_cost: 1.2107 ips: 10.6698 images/s
[02/22 13:04:19] ppdet.engine INFO: Epoch: [3] [0/3] learning_rate: 0.012700 loss_vfl: 0.985893 loss_bbox: 1.105134 loss_dfl: 0.499413 loss: 2.571278 eta: 0:00:41 batch_cost: 1.9723 data_cost: 1.3542 ips: 10.1404 images/s
[02/22 13:04:28] ppdet.engine INFO: Epoch: [4] [0/3] learning_rate: 0.013600 loss_vfl: 0.923104 loss_bbox: 1.060779 loss_dfl: 0.495562 loss: 2.545417 eta: 0:00:36 batch_cost: 2.0552 data_cost: 1.4152 ips: 9.7316 images/s
[02/22 13:04:35] ppdet.engine INFO: Epoch: [5] [0/3] learning_rate: 0.014500 loss_vfl: 0.924571 loss_bbox: 1.001047 loss_dfl: 0.482511 loss: 2.525080 eta: 0:00:30 batch_cost: 2.0249 data_cost: 1.3878 ips: 9.8771 images/s
[02/22 13:04:43] ppdet.engine INFO: Epoch: [6] [0/3] learning_rate: 0.015400 loss_vfl: 0.916189 loss_bbox: 0.956332 loss_dfl: 0.465089 loss: 2.462843 eta: 0:00:24 batch_cost: 2.0367 data_cost: 1.3889 ips: 9.8196 images/s
[02/22 13:04:52] ppdet.engine INFO: Epoch: [7] [0/3] learning_rate: 0.016300 loss_vfl: 0.898742 loss_bbox: 0.872578 loss_dfl: 0.426290 loss: 2.198215 eta: 0:00:18 batch_cost: 2.2244 data_cost: 1.5780 ips: 8.9914 images/s
[02/22 13:05:00] ppdet.engine INFO: Epoch: [8] [0/3] learning_rate: 0.017200 loss_vfl: 0.819227 loss_bbox: 0.769483 loss_dfl: 0.416893 loss: 2.031344 eta: 0:00:12 batch_cost: 2.1973 data_cost: 1.5714 ips: 9.1022 images/s
[02/22 13:05:08] ppdet.engine INFO: Epoch: [9] [0/3] learning_rate: 0.018100 loss_vfl: 0.778409 loss_bbox: 0.735756 loss_dfl: 0.390064 loss: 1.939696 eta: 0:00:06 batch_cost: 2.2483 data_cost: 1.6170 ips: 8.8955 images/s
[02/22 13:05:11] ppdet.utils.checkpoint INFO: Save checkpoint: output/picodet_lcnet_1_5x_416_coco
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
[02/22 13:05:16] ppdet.engine INFO: Eval iter: 0
[02/22 13:05:18] ppdet.metrics.metrics INFO: The bbox result is saved to bbox.json.
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
[02/22 13:05:18] ppdet.metrics.coco_utils INFO: Start evaluate...
Loading and preparing results...
DONE (t=0.01s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=1.35s).
Accumulating evaluation results...
DONE (t=0.02s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.357
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.652
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.273
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.357
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.420
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.490
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.513
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.513
[02/22 13:05:20] ppdet.engine INFO: Total sample number: 20, averge FPS: 3.9130859681580548
[02/22 13:05:20] ppdet.engine INFO: Best test bbox ap is 0.357.
[02/22 13:05:20] ppdet.utils.checkpoint INFO: Save checkpoint: output/picodet_lcnet_1_5x_416_coco
#评估模型
%cd /home/aistudio/work/code/train/
!python3 eval.py -c picodet_lcnet_1_5x_416_coco.yml
/home/aistudio/work/code/train
Warning: import ppdet from source directory without installing, run 'python setup.py install' to install ppdet firstly
W0221 10:42:54.973261 2954 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W0221 10:42:54.978138 2954 device_context.cc:465] device: 0, cuDNN Version: 7.6.
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
[02/21 10:42:58] ppdet.utils.checkpoint INFO: Finish loading model weights: output/picodet_lcnet_1_5x_416_coco/best_model.pdparams
[02/21 10:42:59] ppdet.engine INFO: Eval iter: 0
[02/21 10:43:00] ppdet.metrics.metrics INFO: The bbox result is saved to bbox.json.
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
[02/21 10:43:00] ppdet.metrics.coco_utils INFO: Start evaluate...
Loading and preparing results...
DONE (t=0.01s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=0.29s).
Accumulating evaluation results...
DONE (t=0.02s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.199
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.471
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.076
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.199
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.279
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.401
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.410
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.410
[02/21 10:43:01] ppdet.engine INFO: Total sample number: 20, averge FPS: 9.823824561177508
#如果需要额外导出serving模型，即导出模型时--export_serving_model=true,那么还需要安装paddle_serving的相关包
!pip install paddle-serving-app==0.6.2 -i https://pypi.tuna.tsinghua.edu.cn/simple
!pip install paddle-serving-client==0.6.2 -i https://pypi.tuna.tsinghua.edu.cn/simple
!pip install paddle-serving-server-gpu==0.6.3.post102 -i https://pypi.tuna.tsinghua.edu.cn/simple
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting paddle-serving-app==0.6.2
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/16/51/cbbfd98654e5b5ea1294717f5bef699945881557f623368bd6d321120295/paddle_serving_app-0.6.2-py3-none-any.whl (52 kB)
|████████████████████████████████| 52 kB 1.6 MB/s
[?25hCollecting sentencepiece<=0.1.92
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/1e/05/8d8d8fc7ef89d36bbdc9b4ff808def3db0dd52658e67f16779eb05ddb558/sentencepiece-0.1.92-cp37-cp37m-manylinux1_x86_64.whl (1.2 MB)
|████████████████████████████████| 1.2 MB 3.4 MB/s
[?25hRequirement already satisfied: opencv-python<=4.2.0.32 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddle-serving-app==0.6.2) (4.1.1.26)
Collecting pyclipper
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c5/fa/2c294127e4f88967149a68ad5b3e43636e94e3721109572f8f17ab15b772/pyclipper-1.3.0.post2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (603 kB)
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[?25hRequirement already satisfied: six>=1.10.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddle-serving-app==0.6.2) (1.16.0)
Requirement already satisfied: shapely in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddle-serving-app==0.6.2) (1.8.1.post1)
Requirement already satisfied: pillow in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddle-serving-app==0.6.2) (8.2.0)
Requirement already satisfied: numpy>=1.14.5 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from opencv-python<=4.2.0.32->paddle-serving-app==0.6.2) (1.19.5)
Installing collected packages: sentencepiece, pyclipper, paddle-serving-app
Attempting uninstall: sentencepiece
Found existing installation: sentencepiece 0.1.96
Uninstalling sentencepiece-0.1.96:
Successfully uninstalled sentencepiece-0.1.96
Successfully installed paddle-serving-app-0.6.2 pyclipper-1.3.0.post2 sentencepiece-0.1.92
[33mWARNING: You are using pip version 21.3.1; however, version 22.0.3 is available.
You should consider upgrading via the '/opt/conda/envs/python35-paddle120-env/bin/python -m pip install --upgrade pip' command.[0m
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting paddle-serving-client==0.6.2
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c8/88/5fd2e8deabe660a9128b27d27f368ace8590adc39b3528f93c0f0adf9d56/paddle_serving_client-0.6.2-cp37-none-any.whl (42.3 MB)
|████████████████████████████████| 42.3 MB 195 kB/s
[?25hCollecting grpcio<=1.33.2
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/7a/46/d08d8a5d0e0449f541fe9e7a226854019a41a4fa41fd14332e55b0e4394f/grpcio-1.33.2-cp37-cp37m-manylinux2014_x86_64.whl (3.8 MB)
|████████████████████████████████| 3.8 MB 3.7 MB/s
[?25hRequirement already satisfied: protobuf>=3.11.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddle-serving-client==0.6.2) (3.14.0)
Collecting grpcio-tools<=1.33.2
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/77/1e/91eaee901589ebee04c21df2f551502e7ba946bab99338f77a1f8a4237e1/grpcio_tools-1.33.2-cp37-cp37m-manylinux2014_x86_64.whl (2.5 MB)
|████████████████████████████████| 2.5 MB 5.9 MB/s
[?25hRequirement already satisfied: six>=1.10.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddle-serving-client==0.6.2) (1.16.0)
Requirement already satisfied: requests in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddle-serving-client==0.6.2) (2.24.0)
Requirement already satisfied: numpy>=1.12 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddle-serving-client==0.6.2) (1.19.5)
Requirement already satisfied: chardet<4,>=3.0.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests->paddle-serving-client==0.6.2) (3.0.4)
Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests->paddle-serving-client==0.6.2) (2019.9.11)
Requirement already satisfied: idna<3,>=2.5 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests->paddle-serving-client==0.6.2) (2.8)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests->paddle-serving-client==0.6.2) (1.25.6)
Installing collected packages: grpcio, grpcio-tools, paddle-serving-client
Attempting uninstall: grpcio
Found existing installation: grpcio 1.35.0
Uninstalling grpcio-1.35.0:
Successfully uninstalled grpcio-1.35.0
[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
parl 1.4.1 requires pyzmq==18.1.1, but you have pyzmq 22.3.0 which is incompatible.[0m
Successfully installed grpcio-1.33.2 grpcio-tools-1.33.2 paddle-serving-client-0.6.2
[33mWARNING: You are using pip version 21.3.1; however, version 22.0.3 is available.
You should consider upgrading via the '/opt/conda/envs/python35-paddle120-env/bin/python -m pip install --upgrade pip' command.[0m
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting paddle-serving-server-gpu==0.6.3.post102
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c2/08/cb57da763defad1649a81ef234759f94f5b7f2afb68f74f9e171f5472d07/paddle_serving_server_gpu-0.6.3.post102-py3-none-any.whl (8.2 MB)
|████████████████████████████████| 8.2 MB 519 kB/s
[?25hRequirement already satisfied: grpcio<=1.33.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddle-serving-server-gpu==0.6.3.post102) (1.33.2)
Collecting MarkupSafe==1.1.1
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c2/37/2e4def8ce3739a258998215df907f5815ecd1af71e62147f5eea2d12d4e8/MarkupSafe-1.1.1-cp37-cp37m-manylinux2010_x86_64.whl (33 kB)
Requirement already satisfied: flask>=1.1.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddle-serving-server-gpu==0.6.3.post102) (1.1.1)
Collecting click==7.1.2
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d2/3d/fa76db83bf75c4f8d338c2fd15c8d33fdd7ad23a9b5e57eb6c5de26b430e/click-7.1.2-py2.py3-none-any.whl (82 kB)
|████████████████████████████████| 82 kB 1.4 MB/s
[?25hCollecting Jinja2==2.11.3
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/7e/c2/1eece8c95ddbc9b1aeb64f5783a9e07a286de42191b7204d67b7496ddf35/Jinja2-2.11.3-py2.py3-none-any.whl (125 kB)
|████████████████████████████████| 125 kB 4.4 MB/s
[?25hRequirement already satisfied: itsdangerous==1.1.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddle-serving-server-gpu==0.6.3.post102) (1.1.0)
Requirement already satisfied: protobuf>=3.11.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddle-serving-server-gpu==0.6.3.post102) (3.14.0)
Collecting Werkzeug==1.0.1
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/cc/94/5f7079a0e00bd6863ef8f1da638721e9da21e5bacee597595b318f71d62e/Werkzeug-1.0.1-py2.py3-none-any.whl (298 kB)
|████████████████████████████████| 298 kB 3.7 MB/s
[?25hRequirement already satisfied: grpcio-tools<=1.33.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddle-serving-server-gpu==0.6.3.post102) (1.33.2)
Requirement already satisfied: pyyaml in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddle-serving-server-gpu==0.6.3.post102) (5.1.2)
Collecting func-timeout
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/b3/0d/bf0567477f7281d9a3926c582bfef21bff7498fc0ffd3e9de21811896a0b/func_timeout-4.3.5.tar.gz (44 kB)
|████████████████████████████████| 44 kB 3.3 MB/s
[?25h Preparing metadata (setup.py) ... [?25ldone
[?25hRequirement already satisfied: six>=1.10.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddle-serving-server-gpu==0.6.3.post102) (1.16.0)
Building wheels for collected packages: func-timeout
Building wheel for func-timeout (setup.py) ... [?25ldone
[?25h Created wheel for func-timeout: filename=func_timeout-4.3.5-py3-none-any.whl size=15077 sha256=6001cb8b3998d4816cd583779d540b843aec7558bf4e90d629f2df4445958dc6
Stored in directory: /home/aistudio/.cache/pip/wheels/ea/6b/56/b7bcacbd1bd8cd29883e7f7a29cbd98b87b2d789b25bae5563
Successfully built func-timeout
Installing collected packages: MarkupSafe, Werkzeug, Jinja2, click, func-timeout, paddle-serving-server-gpu
Attempting uninstall: MarkupSafe
Found existing installation: MarkupSafe 2.0.1
Uninstalling MarkupSafe-2.0.1:
Successfully uninstalled MarkupSafe-2.0.1
Attempting uninstall: Werkzeug
Found existing installation: Werkzeug 0.16.0
Uninstalling Werkzeug-0.16.0:
Successfully uninstalled Werkzeug-0.16.0
Attempting uninstall: Jinja2
Found existing installation: Jinja2 2.11.0
Uninstalling Jinja2-2.11.0:
Successfully uninstalled Jinja2-2.11.0
Attempting uninstall: click
Found existing installation: Click 7.0
Uninstalling Click-7.0:
Successfully uninstalled Click-7.0
[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
parl 1.4.1 requires pyzmq==18.1.1, but you have pyzmq 22.3.0 which is incompatible.[0m
Successfully installed Jinja2-2.11.3 MarkupSafe-1.1.1 Werkzeug-1.0.1 click-7.1.2 func-timeout-4.3.5 paddle-serving-server-gpu-0.6.3.post102
[33mWARNING: You are using pip version 21.3.1; however, version 22.0.3 is available.
You should consider upgrading via the '/opt/conda/envs/python35-paddle120-env/bin/python -m pip install --upgrade pip' command.[0m
#导出模型
%cd /home/aistudio/work/code/train/
!python export_model.py --export_serving_model=true -c picodet_lcnet_1_5x_416_coco.yml --output_dir=./output_inference/
/home/aistudio/work/code/train
Warning: import ppdet from source directory without installing, run 'python setup.py install' to install ppdet firstly
[02/22 13:06:15] ppdet.utils.checkpoint INFO: Finish loading model weights: output/picodet_lcnet_1_5x_416_coco/best_model.pdparams
[02/22 13:06:15] ppdet.engine INFO: Export inference config file to ./output_inference/picodet_lcnet_1_5x_416_coco/infer_cfg.yml
W0222 13:06:18.468817 2012 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.0, Runtime API Version: 10.1
W0222 13:06:18.468895 2012 device_context.cc:465] device: 0, cuDNN Version: 7.6.
[02/22 13:06:21] ppdet.engine INFO: Export model and saved in ./output_inference/picodet_lcnet_1_5x_416_coco
导出后会生成serving_server和serving_client文件夹，使用对应的py文件(文件中完成前置处理以及后置处理功能)以及配置文件完成主体检测模型的部署。
上述内容就是使用coco数据集训练一个picodet模型的完整流程。
部署方式可以如图：
部署的config.yml文件内容如图所示
部署的web_service.py文件内容如图所示
%cd /home/aistudio/work/code/train/output_inference/picodet_lcnet_1_5x_416_coco/
%mv serving_server/ /home/aistudio/work/code/picodet_lcnet_1_5x_416_coco/
%mv serving_client/ /home/aistudio/work/code/picodet_lcnet_1_5x_416_coco/
/home/aistudio/work/code/train/output_inference/picodet_lcnet_1_5x_416_coco
#模型部署
%cd /home/aistudio/work/code/picodet_lcnet_1_5x_416_coco/
!python3 web_service.py
/home/aistudio/work/code/picodet_lcnet_1_5x_416_coco
2022/02/21 10:43:36 start proxy service
W0221 10:43:39.671136 3379 analysis_predictor.cc:795] The one-time configuration of analysis predictor failed, which may be due to native predictor called first and its configurations taken effect.
I0221 10:43:39.728690 3379 analysis_predictor.cc:576] TensorRT subgraph engine is enabled
I0221 10:43:39.728780 3379 analysis_predictor.cc:665] ir_optim is turned off, no IR pass will be executed
[1m[35m--- Running analysis [ir_graph_build_pass][0m
[1m[35m--- Running analysis [ir_graph_clean_pass][0m
[1m[35m--- Running analysis [ir_analysis_pass][0m
[1m[35m--- Running analysis [ir_params_sync_among_devices_pass][0m
I0221 10:43:39.807039 3379 ir_params_sync_among_devices_pass.cc:45] Sync params from CPU to GPU
[1m[35m--- Running analysis [adjust_cudnn_workspace_size_pass][0m
[1m[35m--- Running analysis [inference_op_replace_pass][0m
[1m[35m--- Running analysis [memory_optimize_pass][0m
I0221 10:43:39.855954 3379 memory_optimize_pass.cc:216] Cluster name : hardswish_79.tmp_0 size: 102400
I0221 10:43:39.856009 3379 memory_optimize_pass.cc:216] Cluster name : hardswish_53.tmp_0 size: 1228800
I0221 10:43:39.856015 3379 memory_optimize_pass.cc:216] Cluster name : batch_norm_2.tmp_3 size: 19660800
I0221 10:43:39.856031 3379 memory_optimize_pass.cc:216] Cluster name : conv2d_115.tmp_0 size: 2457600
I0221 10:43:39.856045 3379 memory_optimize_pass.cc:216] Cluster name : hardswish_56.tmp_0 size: 204800
I0221 10:43:39.856058 3379 memory_optimize_pass.cc:216] Cluster name : conv2d_105.tmp_0 size: 19660800
I0221 10:43:39.856070 3379 memory_optimize_pass.cc:216] Cluster name : batch_norm_61.tmp_0 size: 512
I0221 10:43:39.856081 3379 memory_optimize_pass.cc:216] Cluster name : depthwise_conv2d_1.tmp_0 size: 4915200
I0221 10:43:39.856084 3379 memory_optimize_pass.cc:216] Cluster name : image size: 4915200
[1m[35m--- Running analysis [ir_graph_to_program_pass][0m
I0221 10:43:40.090010 3379 analysis_predictor.cc:714] ======= optimize end =======
I0221 10:43:40.105052 3379 naive_executor.cc:98] --- skip [feed], feed -> image
I0221 10:43:40.113319 3379 naive_executor.cc:98] --- skip [transpose_0.tmp_0], fetch -> fetch
I0221 10:43:40.113376 3379 naive_executor.cc:98] --- skip [transpose_1.tmp_0], fetch -> fetch
I0221 10:43:40.113399 3379 naive_executor.cc:98] --- skip [transpose_2.tmp_0], fetch -> fetch
I0221 10:43:40.113412 3379 naive_executor.cc:98] --- skip [transpose_3.tmp_0], fetch -> fetch
I0221 10:43:40.113425 3379 naive_executor.cc:98] --- skip [transpose_4.tmp_0], fetch -> fetch
I0221 10:43:40.113430 3379 naive_executor.cc:98] --- skip [transpose_5.tmp_0], fetch -> fetch
I0221 10:43:40.113445 3379 naive_executor.cc:98] --- skip [transpose_6.tmp_0], fetch -> fetch
I0221 10:43:40.113458 3379 naive_executor.cc:98] --- skip [transpose_7.tmp_0], fetch -> fetch
^C
Traceback (most recent call last):
File "web_service.py", line 116, in <module>
ppyolo_mbv3_service.run_service()
File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle_serving_server/web_service.py", line 69, in run_service
self._server.run_server()
File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle_serving_server/pipeline/pipeline_server.py", line 312, in run_server
server.wait_for_termination()
File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/grpc/_server.py", line 979, in wait_for_termination
timeout=timeout)
File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/grpc/_common.py", line 141, in wait
_wait_once(wait_fn, MAXIMUM_WAIT_TIMEOUT, spin_cb)
File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/grpc/_common.py", line 106, in _wait_once
wait_fn(timeout=timeout)
File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/threading.py", line 552, in wait
signaled = self._cond.wait(timeout)
File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/threading.py", line 300, in wait
gotit = waiter.acquire(True, timeout)
KeyboardInterrupt
--------------------------------------
C++ Traceback (most recent call last):
--------------------------------------
No stack trace in paddle, may be caused by external reasons.
----------------------
Error Message Summary:
----------------------
FatalError: `Termination signal` is detected by the operating system.
[TimeInfo: *** Aborted at 1645411431 (unix time) try "date -d @1645411431" if you are using GNU date ***]
[SignalInfo: *** SIGTERM (@0x3e800000cb4) received by PID 3379 (TID 0x7fde098b2700) from PID 3252 ***]
#请求测试
%cd /home/aistudio/work/code/picodet_lcnet_1_5x_416_coco/
!python3 pipeline_http_client.py
/home/aistudio/work/code/picodet_lcnet_1_5x_416_coco
{'err_no': 0, 'err_msg': '', 'key': [], 'value': []}
由于本流程只是教程，因此模型训练只使用了极少部分的数据集以及训练轮次，因此请求不会有任何结果，在实际使用过程中需要大量的数据集和训练时间，其模型请求结果如下。
图像检索库生成与部署
在目标检测模型部署完毕后电瓶车入室检测的功能便可投入使用，但为了提高整体的准确度减少误报则还需要一个额外的检索方式。这里采用了PaddleClas下图像识别中的轻量级通用识别模型general_PPLCNet_x2_5_lite_v1.0_infer，首先根据已有图片集生成格式与下图格式相同的txt的文件，随后在PaddleClas的deploy文件夹下输入命令即可用数据集生成相应的索引库，其中IndexProcess.data_file是下图txt文件的名字，IndexProcess.index_dir是生成 索引库的名字，具体可以查看PaddleClas图像识别快速开始的建立新的索引库部分。（注意，确保build_general.yaml中rec_inference_model_dir路径的模型存在）
#下载模型并转换为serving模型
%cd /home/aistudio/work/code/
!wget -P models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar
%cd models
!tar -xf general_PPLCNet_x2_5_lite_v1.0_infer.tar
!python3 -m paddle_serving_client.convert --dirname ./general_PPLCNet_x2_5_lite_v1.0_infer/ --model_filename inference.pdmodel --params_filename inference.pdiparams --serving_server ./general_PPLCNet_x2_5_lite_v1.0_serving/ --serving_client ./general_PPLCNet_x2_5_lite_v1.0_client/
%cp -r ./general_PPLCNet_x2_5_lite_v1.0_serving ../general_PPLCNet_x2_5_lite_v1.0/
/home/aistudio/work/code
--2022-02-22 13:27:22-- https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar
Resolving paddle-imagenet-models-name.bj.bcebos.com (paddle-imagenet-models-name.bj.bcebos.com)... 182.61.200.195, 182.61.200.229, 2409:8c04:1001:1002:0:ff:b001:368a
Connecting to paddle-imagenet-models-name.bj.bcebos.com (paddle-imagenet-models-name.bj.bcebos.com)|182.61.200.195|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 34242560 (33M) [application/x-tar]
Saving to: ‘models/general_PPLCNet_x2_5_lite_v1.0_infer.tar’
general_PPLCNet_x2_ 100%[===================>] 32.66M 24.2MB/s in 1.3s
2022-02-22 13:27:24 (24.2 MB/s) - ‘models/general_PPLCNet_x2_5_lite_v1.0_infer.tar’ saved [34242560/34242560]
/home/aistudio/work/code/models
%cd /home/aistudio/data/data128448/
!unzip -q index_motorcycle.zip #解压数据集
/home/aistudio/data/data128448
replace index_motorcycle/人/0-10182.jpg? [y]es, [n]o, [A]ll, [N]one, [r]ename: ^C
%cd /home/aistudio/work/code/
!python make_label.py #数据集分类
!python python/build_gallery.py -c build_gallery/build_general.yaml -o IndexProcess.data_file="./index_label.txt" -o IndexProcess.index_dir="index_result" #生成检索库
/home/aistudio/work/code
/home/aistudio/data/data128448/index_motorcycle/摩托车/
/home/aistudio/data/data128448/index_motorcycle/人/
/home/aistudio/data/data128448/index_motorcycle/其他杂物/
/home/aistudio/data/data128448/index_motorcycle/自行车/
2022-02-22 13:27:50 INFO:
===========================================================
== PaddleClas is powered by PaddlePaddle ! ==
===========================================================
== ==
== For more info please go to the following website. ==
== ==
== https://github.com/PaddlePaddle/PaddleClas ==
===========================================================
2022-02-22 13:27:50 INFO: Global :
2022-02-22 13:27:50 INFO: batch_size : 32
2022-02-22 13:27:50 INFO: cpu_num_threads : 10
2022-02-22 13:27:50 INFO: enable_benchmark : True
2022-02-22 13:27:50 INFO: enable_mkldnn : True
2022-02-22 13:27:50 INFO: enable_profile : False
2022-02-22 13:27:50 INFO: gpu_mem : 8000
2022-02-22 13:27:50 INFO: ir_optim : True
2022-02-22 13:27:50 INFO: rec_inference_model_dir : ./models/general_PPLCNet_x2_5_lite_v1.0_infer
2022-02-22 13:27:50 INFO: use_fp16 : False
2022-02-22 13:27:50 INFO: use_gpu : False
2022-02-22 13:27:50 INFO: use_tensorrt : False
2022-02-22 13:27:50 INFO: IndexProcess :
2022-02-22 13:27:50 INFO: data_file : ./index_label.txt
2022-02-22 13:27:50 INFO: delimiter :
2022-02-22 13:27:50 INFO: dist_type : IP
2022-02-22 13:27:50 INFO: embedding_size : 512
2022-02-22 13:27:50 INFO: image_root :
2022-02-22 13:27:50 INFO: index_dir : index_result
2022-02-22 13:27:50 INFO: index_method : HNSW32
2022-02-22 13:27:50 INFO: index_operation : new
2022-02-22 13:27:50 INFO: RecPostProcess : None
2022-02-22 13:27:50 INFO: RecPreProcess :
2022-02-22 13:27:50 INFO: transform_ops :
2022-02-22 13:27:50 INFO: ResizeImage :
2022-02-22 13:27:50 INFO: size : 224
2022-02-22 13:27:50 INFO: NormalizeImage :
2022-02-22 13:27:50 INFO: mean : [0.485, 0.456, 0.406]
2022-02-22 13:27:50 INFO: order :
2022-02-22 13:27:50 INFO: scale : 0.00392157
2022-02-22 13:27:50 INFO: std : [0.229, 0.224, 0.225]
2022-02-22 13:27:50 INFO: ToCHWImage : None
100%|█████████████████████████████████████████| 144/144 [00:05<00:00, 25.24it/s]
2022-02-22 13:27:56 WARNING: The HNSW32 method dose not support 'remove' operation
索引库生成完成后接下来便是的部署，这里我们用到的模型general_PPLCNet_x2_5_lite_v1.0_infer，使用paddleClas中的两个文件便可以部署.
但由于我们只使用了其中的图像检索功能，因此需要将recognization_web_service.py的部分代码进行修改，原始文件在飞桨的github上(https://github.com/PaddlePaddle/PaddleClas/blob/release/2.3/deploy/paddleserving/recognition/recognition_web_service.py)，用于部署图像检索服务的文件在/home/aistudio/work/code/general_PPLCNet_x2_5_lite_v1.0/文件夹下。
修改后的recognization_web_service.py代码如下:
config_onlyrec.yml的内容如下:
%cd /home/aistudio/work/code/
%mv index_result/ general_PPLCNet_x2_5_lite_v1.0/
/home/aistudio/work/code
%cd /home/aistudio/work/code/general_PPLCNet_x2_5_lite_v1.0/
!python recognition_web_service_onlyrec.py #启动服务
/home/aistudio/work/code/general_PPLCNet_x2_5_lite_v1.0
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle_serving_server/pipeline/pipeline_server.py:342: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.
conf = yaml.load(f.read())
config use_encryption model here None
2022/02/22 13:28:11 start proxy service
W0222 13:28:13.646667 5999 analysis_predictor.cc:795] The one-time configuration of analysis predictor failed, which may be due to native predictor called first and its configurations taken effect.
I0222 13:28:13.665211 5999 analysis_predictor.cc:665] ir_optim is turned off, no IR pass will be executed
[1m[35m--- Running analysis [ir_graph_build_pass][0m
[1m[35m--- Running analysis [ir_graph_clean_pass][0m
[1m[35m--- Running analysis [ir_analysis_pass][0m
[1m[35m--- Running analysis [ir_params_sync_among_devices_pass][0m
I0222 13:28:13.716022 5999 ir_params_sync_among_devices_pass.cc:45] Sync params from CPU to GPU
[1m[35m--- Running analysis [adjust_cudnn_workspace_size_pass][0m
[1m[35m--- Running analysis [inference_op_replace_pass][0m
[1m[35m--- Running analysis [memory_optimize_pass][0m
I0222 13:28:13.745045 5999 memory_optimize_pass.cc:216] Cluster name : x size: 602112
I0222 13:28:13.745075 5999 memory_optimize_pass.cc:216] Cluster name : depthwise_conv2d_2.tmp_0 size: 2007040
I0222 13:28:13.745079 5999 memory_optimize_pass.cc:216] Cluster name : batch_norm_24.tmp_1 size: 5120
I0222 13:28:13.745083 5999 memory_optimize_pass.cc:216] Cluster name : conv2d_33.tmp_0 size: 4014080
I0222 13:28:13.745087 5999 memory_optimize_pass.cc:216] Cluster name : batch_norm_2.tmp_3 size: 4014080
[1m[35m--- Running analysis [ir_graph_to_program_pass][0m
I0222 13:28:13.786020 5999 analysis_predictor.cc:714] ======= optimize end =======
I0222 13:28:13.788327 5999 naive_executor.cc:98] --- skip [feed], feed -> x
I0222 13:28:13.789907 5999 naive_executor.cc:98] --- skip [save_infer_model/scale_0.tmp_1], fetch -> fetch
W0222 13:28:17.437884 5999 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.0, Runtime API Version: 10.1
W0222 13:28:17.442231 5999 device_context.cc:465] device: 0, cuDNN Version: 7.6.
[[1.0000004 0.6330568 0.5747858 0.5617034 0.55175585]]
%cd /home/aistudio/work/code/general_PPLCNet_x2_5_lite_v1.0/
ode/general_PPLCNet_x2_5_lite_v1.0/
!python pipeline_http_client.py #请求测试
/home/aistudio/work/code/general_PPLCNet_x2_5_lite_v1.0
0
{'err_no': 0, 'err_msg': '', 'key': ['result'], 'value': ["[{'rec_docs': '摩托车', 'rec_scores': 1.0000004}]"]}
由于使用的原图片请求，因此返回的置信度为1.0，在实际使用场景中同样也有着较高的置信度，结果如图所示。
请点击此处查看本环境基本用法.
Please click here for more detailed instructions.
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