189 lines
4.2 KiB
Markdown
189 lines
4.2 KiB
Markdown
# 服务端预测部署
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`PaddleDetection`训练出来的模型可以使用[Serving](https://github.com/PaddlePaddle/Serving) 部署在服务端。
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本教程以在COCO数据集上用`configs/yolov3/yolov3_darknet53_270e_coco.yml`算法训练的模型进行部署。
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预训练模型权重文件为[yolov3_darknet53_270e_coco.pdparams](https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams) 。
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## 1. 首先验证模型
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```
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python tools/infer.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml --infer_img=demo/000000014439.jpg -o use_gpu=True weights=https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams --infer_img=demo/000000014439.jpg
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```
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## 2. 安装 paddle serving
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请参考[PaddleServing](https://github.com/PaddlePaddle/Serving/tree/v0.7.0) 中安装教程安装（版本>=0.7.0）。
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## 3. 导出模型
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PaddleDetection在训练过程包括网络的前向和优化器相关参数，而在部署过程中，我们只需要前向参数，具体参考:[导出模型](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/deploy/EXPORT_MODEL.md)
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```
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python tools/export_model.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams --export_serving_model=True
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```
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以上命令会在`output_inference/`文件夹下生成一个`yolov3_darknet53_270e_coco`文件夹：
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```
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output_inference
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│ ├── yolov3_darknet53_270e_coco
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│ │ ├── infer_cfg.yml
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│ │ ├── model.pdiparams
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│ │ ├── model.pdiparams.info
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│ │ ├── model.pdmodel
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│ │ ├── serving_client
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│ │ │ ├── serving_client_conf.prototxt
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│ │ │ ├── serving_client_conf.stream.prototxt
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│ │ ├── serving_server
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│ │ │ ├── __model__
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│ │ │ ├── __params__
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│ │ │ ├── serving_server_conf.prototxt
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│ │ │ ├── serving_server_conf.stream.prototxt
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│ │ │ ├── ...
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```
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`serving_client`文件夹下`serving_client_conf.prototxt`详细说明了模型输入输出信息
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`serving_client_conf.prototxt`文件内容为：
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```
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feed_var {
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name: "im_shape"
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alias_name: "im_shape"
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is_lod_tensor: false
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feed_type: 1
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shape: 2
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}
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feed_var {
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name: "image"
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alias_name: "image"
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is_lod_tensor: false
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feed_type: 1
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shape: 3
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shape: 608
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shape: 608
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}
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feed_var {
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name: "scale_factor"
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alias_name: "scale_factor"
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is_lod_tensor: false
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feed_type: 1
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shape: 2
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}
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fetch_var {
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name: "multiclass_nms3_0.tmp_0"
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alias_name: "multiclass_nms3_0.tmp_0"
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is_lod_tensor: true
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fetch_type: 1
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shape: -1
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}
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fetch_var {
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name: "multiclass_nms3_0.tmp_2"
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alias_name: "multiclass_nms3_0.tmp_2"
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is_lod_tensor: false
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fetch_type: 2
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```
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## 4. 启动PaddleServing服务
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```
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cd output_inference/yolov3_darknet53_270e_coco/
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# GPU
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python -m paddle_serving_server.serve --model serving_server --port 9393 --gpu_ids 0
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# CPU
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python -m paddle_serving_server.serve --model serving_server --port 9393
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```
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## 5. 测试部署的服务
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准备`label_list.txt`文件，示例`label_list.txt`文件内容为
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```
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person
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bicycle
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car
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motorcycle
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airplane
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bus
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train
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truck
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boat
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traffic light
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fire hydrant
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stop sign
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parking meter
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bench
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bird
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cat
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dog
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horse
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sheep
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cow
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elephant
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bear
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zebra
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giraffe
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backpack
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umbrella
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handbag
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tie
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suitcase
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frisbee
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skis
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snowboard
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sports ball
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kite
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baseball bat
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baseball glove
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skateboard
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surfboard
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tennis racket
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bottle
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wine glass
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cup
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fork
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knife
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spoon
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bowl
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banana
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apple
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sandwich
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orange
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broccoli
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carrot
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hot dog
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pizza
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donut
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cake
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chair
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couch
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potted plant
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bed
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dining table
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toilet
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tv
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laptop
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mouse
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remote
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keyboard
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cell phone
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microwave
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oven
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toaster
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sink
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refrigerator
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book
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clock
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vase
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scissors
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teddy bear
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hair drier
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toothbrush
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```
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设置`prototxt`文件路径为`serving_client/serving_client_conf.prototxt`
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设置`fetch`为`fetch=["multiclass_nms3_0.tmp_0"])`
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测试
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```
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# 进入目录
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cd output_inference/yolov3_darknet53_270e_coco/
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# 测试代码 test_client.py 会自动创建output文件夹，并在output下生成`bbox.json`和`000000014439.jpg`两个文件
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python ../../deploy/serving/test_client.py ../../deploy/serving/label_list.txt ../../demo/000000014439.jpg
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```
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