使用PaddleX实现车辆检测模型训练到服务化部署落地全流程

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1. 项目背景

随着AI技术的快速发展，深度学习技术在各个领域的应用越来越广泛。作为一种革命性的机器学习算法，深度学习可以自动从大量数据中学习并提取特征，无需手动编写规则，大大提高了人工智能的自动化水平。

但是另一方面，随着AI技术广泛应用，AI技术落困难的问题也日渐显现。根据Gartner的调查发现，只有53%的项目能够从AI原型转化为生产。AI项目落地难一方面是因为AI项目跨部门协作难度大，另一方面是缺乏合适的工具链。而PaddleX作为一个简单易用的深度学习平台，让用户能够快速构建和训练深度学习模型，而无需关心底层的技术细节。为了实现这个目标，PaddleX 提供了丰富的模型和算法库，包括图像分类、目标检测、实例分割等，同时也提供了易用的工具和接口，如模型转换工具、数据集工具、可视化工具等，用户只需要通过简单的命令行或图形界面操作，就可以完成模型的训练和部署。

本项目基于开源的数据使用paddex的API训练模型，之后再将模型导出并转换为部署模型，并通过paddlex内置的paddlehub将模型的推理服务进行服务化部署。通过在后台发布RestfulAPI接口，让其他程序（服务）可以通过http协议进行调用，从而完成一个完整的AI模型从训练到落地部署的整个流程。

2. 数据查看

使用coco数据集中的抽取的car, bus, truck三个类别作为训练和验证数据集，同时提供相应的标注文件

!unzip -oq /home/aistudio/data/data171362/car_coco.zip -d /home/aistudio/work

3. 开发环境准备

本项目使用paddleX的API开发模型来构建和准换模型，下载安装paddleX即可

# 下载paddleX
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4. 使用paddleX训练车辆检测模型

# 查看环境内各个模块的版本
import paddle, paddlex, paddlehub
print(paddle.__version__)
print(paddlex.__version__)
print(paddlehub.__version__)

4.1 定义数据集

本项目的数据集使用coco格式的数据标注，使用paddleX进行训练时，使用CocoDatectionAPI接口

该接口的参数如下：

paddlex.datasets.CocoDetection(data_dir, ann_file, transforms=None, num_workers=‘auto’, shuffle=False, allow_empty=False, empty_ratio=1.)

import paddlex as pdx
from paddlex import transforms as Ttrain_transforms = T.Compose([T.Resize(512),T.RandomHorizontalFlip(),T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])eval_transforms = T.Compose([T.Resize(512),T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])train_dataset = pdx.datasets.CocoDetection(data_dir='/home/aistudio/work/train2017',ann_file='/home/aistudio/work/annotations/train2017.json',transforms=train_transforms)
eval_dataset = pdx.datasets.CocoDetection(data_dir='/home/aistudio/work/val2017',ann_file='/home/aistudio/work/annotations/val2017.json',transforms=eval_transforms)

loading annotations into memory...
Done (t=0.36s)
creating index...
index created!
2023-06-01 10:20:32 [INFO]  Starting to read file list from dataset...
2023-06-01 10:20:32 [INFO]  16270 samples in file /home/aistudio/work/annotations/train2017.json, including 16270 positive samples and 0 negative samples.
loading annotations into memory...
Done (t=0.25s)
creating index...
index created!
2023-06-01 10:20:33 [INFO]  Starting to read file list from dataset...
2023-06-01 10:20:33 [INFO]  707 samples in file /home/aistudio/work/annotations/val2017.json, including 707 positive samples and 0 negative samples.

4.2. 组网训练，使用经典的YOLOV3目标检测网络

使用经典的YOLO V3模型，入网尺寸为512*512，在Telsa V100 32G环境下可以设置bs=64

num_classes = len(train_dataset.labels)
model = pdx.det.YOLOv3(num_classes=num_classes, backbone='MobileNetV3')
model.train(num_epochs=80,train_dataset=train_dataset,train_batch_size=48,eval_dataset=eval_dataset,pretrain_weights='COCO',learning_rate=0.00001,warmup_steps=1000,warmup_start_lr=0.0,lr_decay_epochs=[35, 50, 90],save_interval_epochs=5,save_dir='output/yolov3_darknet53')

2023-06-01 10:20:37 [INFO]   Downloading yolov3_mobilenet_v3_large_270e_coco.pdparams from https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_270e_coco.pdparams100%|██████████| 139189/139189 [00:11<00:00, 11694.77KB/s]2023-06-01 10:20:52 [INFO]    Loading pretrained model from output/yolov3_darknet53/pretrain/yolov3_mobilenet_v3_large_270e_coco.pdparams
2023-06-01 10:20:53 [WARNING]   [SKIP] Shape of pretrained params yolo_head.yolo_output.0.weight doesn't match.(Pretrained: [255, 1024, 1, 1], Actual: [24, 1024, 1, 1])
2023-06-01 10:20:53 [WARNING]   [SKIP] Shape of pretrained params yolo_head.yolo_output.0.bias doesn't match.(Pretrained: [255], Actual: [24])
2023-06-01 10:20:53 [WARNING]   [SKIP] Shape of pretrained params yolo_head.yolo_output.1.weight doesn't match.(Pretrained: [255, 512, 1, 1], Actual: [24, 512, 1, 1])
2023-06-01 10:20:53 [WARNING]   [SKIP] Shape of pretrained params yolo_head.yolo_output.1.bias doesn't match.(Pretrained: [255], Actual: [24])
2023-06-01 10:20:53 [WARNING]   [SKIP] Shape of pretrained params yolo_head.yolo_output.2.weight doesn't match.(Pretrained: [255, 256, 1, 1], Actual: [24, 256, 1, 1])
2023-06-01 10:20:53 [WARNING]   [SKIP] Shape of pretrained params yolo_head.yolo_output.2.bias doesn't match.(Pretrained: [255], Actual: [24])
2023-06-01 10:20:53 [INFO]  There are 362/368 variables loaded into YOLOv3.

训练过程中，训练的log位于output/yolov3_darknet53/vdl_log目录下，可以使用可视化服务同步开启训练过程可视化，监控loss和mAP等指标的变化

loss_cls	lr	bbox_mmAP

5. 导出部署模型

在使用PaddleX 2.0及以上版本进行训练时通过设置save_interval_epochs参数可以在训练过程中，保存训练阶段的模型。
在保存的模型文件夹中，主要包含四个文件：

model.pdopt，训练模型参数的优化器
model.pdparams，模型参数
model.yml，模型的配置文件（包括预处理参数、模型定义等）
eval_details.json，模型评估时的预测结果和真值

需要注意的是，训练保存的模型不能直接用于部署，需要导出成部署格式后才能用于部署。

在服务端部署模型时需要将训练过程中保存的模型导出为inference格式模型，在paddleX中直接使用export_inference这个API即可将训练模型导出
导出的inference格式模型包括五个文件：

model.pdmodel，模型网络结构
model.pdiparams，模型权重
model.pdiparams.info，模型权重名称
model.yml，模型的配置文件（包括预处理参数、模型定义等）
pipeline.yml，可用于PaddleX Manufacture SDK的流程配置文件

下面导出本项目的部署模型

%cd /home/aistudio
!paddlex --export_inference --model_dir=/home/aistudio/output/yolov3_darknet53/best_model/ --save_dir=./inference_model

/home/aistudio
[05-24 14:08:02 MainThread @logger.py:242] Argv: /opt/conda/envs/python35-paddle120-env/bin/paddlex --export_inference --model_dir=/home/aistudio/output/yolov3_darknet53/best_model/ --save_dir=./inference_model
[05-24 14:08:02 MainThread @utils.py:79] WRN paddlepaddle version: 2.3.2. The dynamic graph version of PARL is under development, not fully tested and supported
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/parl/remote/communication.py:38: FutureWarning: 'pyarrow.default_serialization_context' is deprecated as of 2.0.0 and will be removed in a future version. Use pickle or the pyarrow IPC functionality instead.context = pyarrow.default_serialization_context()
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop workingfrom collections import MutableMapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop workingfrom collections import Iterable, Mapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop workingfrom collections import Sized
2023-05-24 14:08:03,055-WARNING: type object 'QuantizationTransformPass' has no attribute '_supported_quantizable_op_type'
2023-05-24 14:08:03,056-WARNING: If you want to use training-aware and post-training quantization, please use Paddle >= 1.8.4 or develop version
W0524 14:08:04.458117  4264 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 11.2
W0524 14:08:04.462399  4264 gpu_resources.cc:91] device: 0, cuDNN Version: 8.2.
2023-05-24 14:08:04 [INFO]  Model[YOLOv3] loaded.
2023-05-24 14:08:09 [INFO]  The model for the inference deployment is saved in ./inference_model/inference_model.

# 查看导出的文件结构
!tree -L 1 /home/aistudio/inference_model/inference_model

/home/aistudio/inference_model/inference_model
├── model.pdiparams
├── model.pdiparams.info
├── model.pdmodel
├── model.yml
└── pipeline.yml0 directories, 5 files

6. 模型转换

借助PaddleHub-Serving，可以将PaddleX的Inference Model进行快速部署，以提供在线预测的能力。

要实现在线部署，需要将PaddleX的Inference Model转换成PaddleHub的预训练模型，使用命令hub convert即可一键转换，对此命令的说明如下：

格式： hub convert --model_dir --module_name --module_version --output_dir
参数：
- –model_dir PaddleX Inference Model所在的目录
- –module_name 生成预训练模型的名称
- –module_version 生成预训练模型的版本，默认为1.0.0
- –output_dir 生成预训练模型的存放位置，默认为{module_name}_{timestamp}

下面代码演示本项目的模型转换

!hub convert --model_dir /home/aistudio/inference_model/inference_model \--module_name car_det \--module_version 1.0

The converted module is stored in `car_det_1684908581.863285`.

7. 安装模型

使用hub conver API进行模型转后得到了一个.tar.gz格式的预训练模型压缩包，在进行部署之前需要先安装到本机，使用命令hub install即可一键安装，对此命令的说明如下：

格式： hub install {MODULE_DIR}
其中MODULE_DIR为要安装的预训练模型文件路径。

安装成功后会打印提示信息，如下：

Successfully installed Successfully installed xxxx

下面代码进行本项目的模型安装。另外需要注意，如果读者使用自己训练并导出转换的模型，需要将下面的代码中的路径修改成自己的模型路径。
同时为了方便用户体验，本项目下也挂载了转换好的模型用于安装体验，运行下面的代码即可

安装完成后可以在当前环境中的.paddlehub/module/model目录下看到安装的模型

!hub install /home/aistudio/car_det_1684908581.863285/car_det.tar.gz

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/setuptools/depends.py:2: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative usesimport imp
Decompress /home/aistudio/car_det_1684908581.863285/car_det.tar.gz
[##################################################] 100.00%
[32m[06-01 10:24:15 MainThread @logger.py:242][0m Argv: /opt/conda/envs/python35-paddle120-env/bin/hub install /home/aistudio/car_det_1684908581.863285/car_det.tar.gz
[0m[33m[06-01 10:24:15 MainThread @utils.py:79][0m [5m[33mWRN[0m paddlepaddle version: 2.3.2. The dynamic graph version of PARL is under development, not fully tested and supported
[0m2023-06-01 10:24:16,422-WARNING: type object 'QuantizationTransformPass' has no attribute '_supported_quantizable_op_type'
[0m2023-06-01 10:24:16,423-WARNING: If you want to use training-aware and post-training quantization, please use Paddle >= 1.8.4 or develop version
[0m[32m[2023-06-01 10:24:17,248] [    INFO][0m - Successfully installed car_det-1.0[0m
[0m

# 安装完成后，可以查看目录文件
!tree -L 2 /home/aistudio/.paddlehub/modules

/home/aistudio/.paddlehub/modules
└── car_det├── assets├── __init__.py├── module.py├── __pycache__└── serving_client_demo.py3 directories, 3 files

8. 一键部署模型发布

在终端使用 hub serving start -m car_det 进行发布, 运行后，见到如下提示表明服务开启成功

paddlehub使用Flask作为web服务后端，默认的服务端口为8866。发布时可以同时使用 --port参数指定端口。

9. 客户端发送请求验证推理服务

后台服务启动后，我们可以在前端通过post请求的方式传入需要推理的图片数据，然后解析返回后的数据即可得到推理结果。
通过RESTful API接口发布AI推理服务，可以使得服务更加模块化、灵活和安全，可以广泛应用于各种场景中。

下面的演示基于paddlehub提供的推理脚本，向后台post请求并处理返回结果，以及可视化推理结果

9.1 单图预测

# coding: utf8
%matplotlib inline
import matplotlib.pyplot as plt # plt 用于显示图片
import requests
import json
import cv2
import base64
import numpy as np
import colorsys
import warnings
warnings.filterwarnings("ignore")from paddlex.det import visualizedef cv2_to_base64(image):data = cv2.imencode('.jpg', image)[1]return base64.b64encode(data.tostring()).decode('utf8')if __name__ == '__main__':# 获取图片的base64编码格式img_path = "/home/aistudio/demo_image/test1.jpg"img1 = cv2_to_base64(cv2.imread(img_path))data = {'images': [img1]}# 指定content-typeheaders = {"Content-type": "application/json"}# 发送HTTP请求url = "http://127.0.0.1:8866/predict/car_det"r = requests.post(url=url, headers=headers, data=json.dumps(data))# 打印预测结果，注意，r.json()["results"]本身就是一个数组，要取到对应图片的预测结果，需指定元素位置，如r.json()["results"][0]print(r.json()["results"][0])# 使用重写的visualize()方法完成预测结果后处理# 显示第一张图片的预测效果image = visualize(cv2.imread(img_path),r.json()["results"][0], threshold=0.2,save_dir=None)image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)plt.imshow(image)plt.axis('off') # 不显示坐标轴plt.show()

[{'bbox': [675.3522338867188, 633.1513671875, 109.446533203125, 96.7626953125], 'category': 'car', 'category_id': 0, 'score': 0.9230645895004272}, {'bbox': [1068.6134033203125, 717.2249145507812, 128.878662109375, 79.77508544921875], 'category': 'car', 'category_id': 0, 'score': 0.8694654703140259}, {'bbox': [817.390869140625, 636.6602783203125, 136.7486572265625, 107.619873046875], 'category': 'car', 'category_id': 0, 'score': 0.8162567019462585}, {'bbox': [362.31573486328125, 581.7606201171875, 125.5926513671875, 93.572998046875], 'category': 'car', 'category_id': 0, 'score': 0.7825871109962463}, {'bbox': [315.59149169921875, 734.1367797851562, 148.85296630859375, 62.86322021484375], 'category': 'car', 'category_id': 0, 'score': 0.7481681108474731}, {'bbox': [184.5955352783203, 516.3251953125, 99.91166687011719, 89.7103271484375], 'category': 'car', 'category_id': 0, 'score': 0.7179760932922363}, {'bbox': [750.9552612304688, 513.614501953125, 99.6092529296875, 80.00146484375], 'categ

通过可视化可以看到大部分的目标都可以检测到，但是对于一些比较拥挤的小目标，召回较低。
上面的可视化推理脚本中设置了threshold=0.2，这个是将返回结果中置信度小于0.2的bbox过滤掉，在实际上线前，需要通过一些边界来测试寻找合适的阈值

9.2 批量预测

将需要预测的批量图片传入列表即可实现批量预测

# coding: utf8
%matplotlib inline
import os
import matplotlib.pyplot as plt # plt 用于显示图片
import requests
import json
import cv2
import base64
import numpy as np
import colorsys
import warnings
warnings.filterwarnings("ignore")from paddlex.det import visualizedef cv2_to_base64(image):data = cv2.imencode('.jpg', image)[1]return base64.b64encode(data.tostring()).decode('utf8')if __name__ == '__main__':# 获取图片的base64编码格式img_root = "/home/aistudio/demo_image/"img_list = os.listdir(img_root)imgs_path = [os.path.join(img_root, img) for img in img_list if img.endswith((".png", ".jpg", ".bmp"))]img_data = [cv2_to_base64(cv2.imread(img_path)) for img_path in imgs_path]data = {'images': img_data}print("There are {} images in the data".format(len(img_data)))# 指定content-typeheaders = {"Content-type": "application/json"}# 发送HTTP请求url = "http://127.0.0.1:8866/predict/car_det"r = requests.post(url=url, headers=headers, data=json.dumps(data))# 使用重写的visualize()方法完成预测结果后处理# 显示所有图片的预测效果for index, img_path in enumerate(imgs_path):image = visualize(cv2.imread(img_path),r.json()["results"][index], threshold=0.15,save_dir=None)image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)plt.imshow(image)plt.axis('off') # 不显示坐标轴plt.show()

There are 5 images in the data

.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.imshow(image)
plt.axis(‘off’) # 不显示坐标轴
plt.show()

There are 5 images in the data

可以看到同样的问题，即对小目标和拥挤程度比较高的目标检测能力有限，这个是由于YOLOV3网络的原理决定的。读者可以本项目的基础上尝试ppyolo系列网络，该系列网络在YOLOV3的基础上做了很多优化，检测效果更佳！

10. 总结

本项目展示使用paddleX全流程AI开发工具来处理AI模型两个最关键的问题：训练和部署。
通过使用API一键调用的方式，将繁琐的流程简化，让开发者将更多的精力放在优化数据和业务上，从而有效的推动AI项目的落地提供生产力价值。

关于作者：

wolfmax老狼，PPDE， AICA六期学员
某半导体CIM软件集成商图像算法工程师，主要方向为图像相关相关的检测分割等算法开发
我在AI Studio上获得钻石等级，点亮7个徽章，来互关呀~ https://aistudio.baidu.com/aistudio/personalcenter/thirdview/801106

此文章为搬运
原项目链接