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116 lines
3.6 KiB
Markdown
116 lines
3.6 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/v0.5.0) 中安装教程安装
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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=weights/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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lient_conf.prototxt
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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: "save_infer_model/scale_0.tmp_1"
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alias_name: "save_infer_model/scale_0.tmp_1"
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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: "save_infer_model/scale_1.tmp_1"
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alias_name: "save_infer_model/scale_1.tmp_1"
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is_lod_tensor: true
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fetch_type: 2
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shape: -1
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}
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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_gpu.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
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```
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# 进入到导出模型文件夹
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cd output_inference/yolov3_darknet53_270e_coco/
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# 将数据集对应的label_list.txt文件放到当前文件夹下
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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=["save_infer_model/scale_0.tmp_1"])`
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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 ../../demo/000000014439.jpg
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```
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