更换文档检测模型
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188
paddle_detection/deploy/serving/README.md
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188
paddle_detection/deploy/serving/README.md
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# 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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74
paddle_detection/deploy/serving/cpp/README.md
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paddle_detection/deploy/serving/cpp/README.md
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# C++ Serving预测部署
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## 1. 简介
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Paddle Serving是飞桨开源的服务化部署框架，提供了C++ Serving和Python Pipeline两套框架，
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C++ Serving框架更倾向于追求极致性能，Python Pipeline框架倾向于二次开发的便捷性。
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旨在帮助深度学习开发者和企业提供高性能、灵活易用的工业级在线推理服务，助力人工智能落地应用。
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更多关于Paddle Serving的介绍，可以参考[Paddle Serving官网repo](https://github.com/PaddlePaddle/Serving)。
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本文档主要介绍利用C++ Serving框架实现模型（以yolov3_darknet53_270e_coco为例）的服务化部署。
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## 2. C++ Serving预测部署
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#### 2.1 C++ 服务化部署样例程序介绍
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服务化部署的样例程序的目录地址为：`deploy/serving/cpp`
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```shell
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deploy/
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├── serving/
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│ ├── python/ # Python 服务化部署样例程序目录
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│ │ ├──config.yml # 服务端模型预测相关配置文件
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│ │ ├──pipeline_http_client.py # 客户端代码
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│ │ ├──postprocess_ops.py # 用户自定义后处理代码
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│ │ ├──preprocess_ops.py # 用户自定义预处理代码
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│ │ ├──README.md # 说明文档
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│ │ ├──web_service.py # 服务端代码
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│ ├── cpp/ # C++ 服务化部署样例程序目录
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│ │ ├──preprocess/ # C++ 自定义OP
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│ │ ├──build_server.sh # C++ Serving 编译脚本
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│ │ ├──serving_client.py # 客户端代码
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│ │ └── ...
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│ └── ...
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└── ...
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```
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### 2.2 环境准备
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安装Paddle Serving三个安装包的最新版本，
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分别是：paddle-serving-client, paddle-serving-app和paddlepaddle(CPU/GPU版本二选一)。
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```commandline
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pip install paddle-serving-client
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# pip install paddle-serving-server # CPU
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pip install paddle-serving-server-gpu # GPU 默认 CUDA10.2 + TensorRT6，其他环境需手动指定版本号
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pip install paddle-serving-app
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# pip install paddlepaddle # CPU
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pip install paddlepaddle-gpu
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```
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您可能需要使用国内镜像源（例如百度源, 在pip命令中添加`-i https://mirror.baidu.com/pypi/simple`）来加速下载。
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Paddle Serving Server更多不同运行环境的whl包下载地址，请参考：[下载页面](https://github.com/PaddlePaddle/Serving/blob/v0.7.0/doc/Latest_Packages_CN.md)
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PaddlePaddle更多版本请参考[官网](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html)
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### 2.3 服务化部署模型导出
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导出步骤参考文档[PaddleDetection部署模型导出教程](../../EXPORT_MODEL.md),
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导出服务化部署模型需要添加`--export_serving_model True`参数，导出示例如下:
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```commandline
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python tools/export_model.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml \
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--export_serving_model True \
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-o weights=https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams
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```
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### 2.4 编译C++ Serving & 启动服务端模型预测服务
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可使用一键编译脚本`deploy/serving/cpp/build_server.sh`进行编译
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```commandline
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bash deploy/serving/cpp/build_server.sh
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```
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当完成以上编译安装和模型导出后，可以按如下命令启动模型预测服务：
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```commandline
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python -m paddle_serving_server.serve --model output_inference/yolov3_darknet53_270e_coco/serving_server --op yolov3_darknet53_270e_coco --port 9997 &
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```
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如果需要自定义开发OP，请参考[文档](https://github.com/PaddlePaddle/Serving/blob/v0.8.3/doc/C%2B%2B_Serving/2%2B_model.md)进行开发
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### 2.5 启动客户端访问
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当成功启动了模型预测服务，可以按如下命令启动客户端访问服务：
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```commandline
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python deploy/serving/python/serving_client.py --serving_client output_inference/yolov3_darknet53_270e_coco/serving_client --image_file demo/000000014439.jpg --http_port 9997
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```
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70
paddle_detection/deploy/serving/cpp/build_server.sh
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paddle_detection/deploy/serving/cpp/build_server.sh
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#使用镜像：
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#registry.baidubce.com/paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82
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#编译Serving Server：
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#client和app可以直接使用release版本
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#server因为加入了自定义OP，需要重新编译
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apt-get update
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apt install -y libcurl4-openssl-dev libbz2-dev
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wget https://paddle-serving.bj.bcebos.com/others/centos_ssl.tar && tar xf centos_ssl.tar && rm -rf centos_ssl.tar && mv libcrypto.so.1.0.2k /usr/lib/libcrypto.so.1.0.2k && mv libssl.so.1.0.2k /usr/lib/libssl.so.1.0.2k && ln -sf /usr/lib/libcrypto.so.1.0.2k /usr/lib/libcrypto.so.10 && ln -sf /usr/lib/libssl.so.1.0.2k /usr/lib/libssl.so.10 && ln -sf /usr/lib/libcrypto.so.10 /usr/lib/libcrypto.so && ln -sf /usr/lib/libssl.so.10 /usr/lib/libssl.so
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# 安装go依赖
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rm -rf /usr/local/go
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wget -qO- https://paddle-ci.cdn.bcebos.com/go1.17.2.linux-amd64.tar.gz | tar -xz -C /usr/local
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export GOROOT=/usr/local/go
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export GOPATH=/root/gopath
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export PATH=$PATH:$GOPATH/bin:$GOROOT/bin
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go env -w GO111MODULE=on
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go env -w GOPROXY=https://goproxy.cn,direct
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go install github.com/grpc-ecosystem/grpc-gateway/protoc-gen-grpc-gateway@v1.15.2
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go install github.com/grpc-ecosystem/grpc-gateway/protoc-gen-swagger@v1.15.2
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go install github.com/golang/protobuf/protoc-gen-go@v1.4.3
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go install google.golang.org/grpc@v1.33.0
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go env -w GO111MODULE=auto
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# 下载opencv库
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wget https://paddle-qa.bj.bcebos.com/PaddleServing/opencv3.tar.gz && tar -xvf opencv3.tar.gz && rm -rf opencv3.tar.gz
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export OPENCV_DIR=$PWD/opencv3
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# clone Serving
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git clone https://github.com/PaddlePaddle/Serving.git -b develop --depth=1
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cd Serving
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export Serving_repo_path=$PWD
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git submodule update --init --recursive
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python -m pip install -r python/requirements.txt
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# set env
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export PYTHON_INCLUDE_DIR=$(python -c "from distutils.sysconfig import get_python_inc; print(get_python_inc())")
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export PYTHON_LIBRARIES=$(python -c "import distutils.sysconfig as sysconfig; print(sysconfig.get_config_var('LIBDIR'))")
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export PYTHON_EXECUTABLE=`which python`
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export CUDA_PATH='/usr/local/cuda'
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export CUDNN_LIBRARY='/usr/local/cuda/lib64/'
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export CUDA_CUDART_LIBRARY='/usr/local/cuda/lib64/'
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export TENSORRT_LIBRARY_PATH='/usr/local/TensorRT6-cuda10.1-cudnn7/targets/x86_64-linux-gnu/'
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# cp 自定义OP代码
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\cp ../deploy/serving/cpp/preprocess/*.h ${Serving_repo_path}/core/general-server/op
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\cp ../deploy/serving/cpp/preprocess/*.cpp ${Serving_repo_path}/core/general-server/op
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# 编译Server, export SERVING_BIN
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mkdir server-build-gpu-opencv && cd server-build-gpu-opencv
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cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR \
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-DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \
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-DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \
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-DCUDA_TOOLKIT_ROOT_DIR=${CUDA_PATH} \
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-DCUDNN_LIBRARY=${CUDNN_LIBRARY} \
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-DCUDA_CUDART_LIBRARY=${CUDA_CUDART_LIBRARY} \
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-DTENSORRT_ROOT=${TENSORRT_LIBRARY_PATH} \
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-DOPENCV_DIR=${OPENCV_DIR} \
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-DWITH_OPENCV=ON \
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-DSERVER=ON \
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-DWITH_GPU=ON ..
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make -j32
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python -m pip install python/dist/paddle*
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export SERVING_BIN=$PWD/core/general-server/serving
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cd ../../
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@@ -0,0 +1,309 @@
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "core/general-server/op/mask_rcnn_r50_fpn_1x_coco.h"
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#include "core/predictor/framework/infer.h"
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#include "core/predictor/framework/memory.h"
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#include "core/predictor/framework/resource.h"
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#include "core/util/include/timer.h"
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#include <algorithm>
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#include <iostream>
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#include <memory>
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#include <sstream>
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namespace baidu {
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namespace paddle_serving {
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namespace serving {
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using baidu::paddle_serving::Timer;
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using baidu::paddle_serving::predictor::InferManager;
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using baidu::paddle_serving::predictor::MempoolWrapper;
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using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
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using baidu::paddle_serving::predictor::general_model::Request;
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using baidu::paddle_serving::predictor::general_model::Response;
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using baidu::paddle_serving::predictor::general_model::Tensor;
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int mask_rcnn_r50_fpn_1x_coco::inference() {
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VLOG(2) << "Going to run inference";
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const std::vector<std::string> pre_node_names = pre_names();
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if (pre_node_names.size() != 1) {
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LOG(ERROR) << "This op(" << op_name()
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<< ") can only have one predecessor op, but received "
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<< pre_node_names.size();
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return -1;
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||||
}
|
||||
const std::string pre_name = pre_node_names[0];
|
||||
|
||||
const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name);
|
||||
if (!input_blob) {
|
||||
LOG(ERROR) << "input_blob is nullptr,error";
|
||||
return -1;
|
||||
}
|
||||
uint64_t log_id = input_blob->GetLogId();
|
||||
VLOG(2) << "(logid=" << log_id << ") Get precedent op name: " << pre_name;
|
||||
|
||||
GeneralBlob *output_blob = mutable_data<GeneralBlob>();
|
||||
if (!output_blob) {
|
||||
LOG(ERROR) << "output_blob is nullptr,error";
|
||||
return -1;
|
||||
}
|
||||
output_blob->SetLogId(log_id);
|
||||
|
||||
if (!input_blob) {
|
||||
LOG(ERROR) << "(logid=" << log_id
|
||||
<< ") Failed mutable depended argument, op:" << pre_name;
|
||||
return -1;
|
||||
}
|
||||
|
||||
const TensorVector *in = &input_blob->tensor_vector;
|
||||
TensorVector *out = &output_blob->tensor_vector;
|
||||
|
||||
int batch_size = input_blob->_batch_size;
|
||||
output_blob->_batch_size = batch_size;
|
||||
VLOG(2) << "(logid=" << log_id << ") infer batch size: " << batch_size;
|
||||
|
||||
Timer timeline;
|
||||
int64_t start = timeline.TimeStampUS();
|
||||
timeline.Start();
|
||||
|
||||
// only support string type
|
||||
char *total_input_ptr = static_cast<char *>(in->at(0).data.data());
|
||||
std::string base64str = total_input_ptr;
|
||||
|
||||
cv::Mat img = Base2Mat(base64str);
|
||||
cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
|
||||
|
||||
// preprocess
|
||||
Resize(&img, scale_factor_h, scale_factor_w, im_shape_h, im_shape_w);
|
||||
Normalize(&img, mean_, scale_, is_scale_);
|
||||
PadStride(&img, 32);
|
||||
int input_shape_h = img.rows;
|
||||
int input_shape_w = img.cols;
|
||||
std::vector<float> input(1 * 3 * input_shape_h * input_shape_w, 0.0f);
|
||||
Permute(img, input.data());
|
||||
|
||||
// create real_in
|
||||
TensorVector *real_in = new TensorVector();
|
||||
if (!real_in) {
|
||||
LOG(ERROR) << "real_in is nullptr,error";
|
||||
return -1;
|
||||
}
|
||||
|
||||
int in_num = 0;
|
||||
size_t databuf_size = 0;
|
||||
void *databuf_data = NULL;
|
||||
char *databuf_char = NULL;
|
||||
|
||||
// im_shape
|
||||
std::vector<float> im_shape{static_cast<float>(im_shape_h),
|
||||
static_cast<float>(im_shape_w)};
|
||||
databuf_size = 2 * sizeof(float);
|
||||
|
||||
databuf_data = MempoolWrapper::instance().malloc(databuf_size);
|
||||
if (!databuf_data) {
|
||||
LOG(ERROR) << "Malloc failed, size: " << databuf_size;
|
||||
return -1;
|
||||
}
|
||||
|
||||
memcpy(databuf_data, im_shape.data(), databuf_size);
|
||||
databuf_char = reinterpret_cast<char *>(databuf_data);
|
||||
paddle::PaddleBuf paddleBuf_0(databuf_char, databuf_size);
|
||||
paddle::PaddleTensor tensor_in_0;
|
||||
tensor_in_0.name = "im_shape";
|
||||
tensor_in_0.dtype = paddle::PaddleDType::FLOAT32;
|
||||
tensor_in_0.shape = {1, 2};
|
||||
tensor_in_0.lod = in->at(0).lod;
|
||||
tensor_in_0.data = paddleBuf_0;
|
||||
real_in->push_back(tensor_in_0);
|
||||
|
||||
// image
|
||||
in_num = 1 * 3 * input_shape_h * input_shape_w;
|
||||
databuf_size = in_num * sizeof(float);
|
||||
|
||||
databuf_data = MempoolWrapper::instance().malloc(databuf_size);
|
||||
if (!databuf_data) {
|
||||
LOG(ERROR) << "Malloc failed, size: " << databuf_size;
|
||||
return -1;
|
||||
}
|
||||
|
||||
memcpy(databuf_data, input.data(), databuf_size);
|
||||
databuf_char = reinterpret_cast<char *>(databuf_data);
|
||||
paddle::PaddleBuf paddleBuf_1(databuf_char, databuf_size);
|
||||
paddle::PaddleTensor tensor_in_1;
|
||||
tensor_in_1.name = "image";
|
||||
tensor_in_1.dtype = paddle::PaddleDType::FLOAT32;
|
||||
tensor_in_1.shape = {1, 3, input_shape_h, input_shape_w};
|
||||
tensor_in_1.lod = in->at(0).lod;
|
||||
tensor_in_1.data = paddleBuf_1;
|
||||
real_in->push_back(tensor_in_1);
|
||||
|
||||
// scale_factor
|
||||
std::vector<float> scale_factor{scale_factor_h, scale_factor_w};
|
||||
databuf_size = 2 * sizeof(float);
|
||||
|
||||
databuf_data = MempoolWrapper::instance().malloc(databuf_size);
|
||||
if (!databuf_data) {
|
||||
LOG(ERROR) << "Malloc failed, size: " << databuf_size;
|
||||
return -1;
|
||||
}
|
||||
|
||||
memcpy(databuf_data, scale_factor.data(), databuf_size);
|
||||
databuf_char = reinterpret_cast<char *>(databuf_data);
|
||||
paddle::PaddleBuf paddleBuf_2(databuf_char, databuf_size);
|
||||
paddle::PaddleTensor tensor_in_2;
|
||||
tensor_in_2.name = "scale_factor";
|
||||
tensor_in_2.dtype = paddle::PaddleDType::FLOAT32;
|
||||
tensor_in_2.shape = {1, 2};
|
||||
tensor_in_2.lod = in->at(0).lod;
|
||||
tensor_in_2.data = paddleBuf_2;
|
||||
real_in->push_back(tensor_in_2);
|
||||
|
||||
if (InferManager::instance().infer(engine_name().c_str(), real_in, out,
|
||||
batch_size)) {
|
||||
LOG(ERROR) << "(logid=" << log_id
|
||||
<< ") Failed do infer in fluid model: " << engine_name().c_str();
|
||||
return -1;
|
||||
}
|
||||
|
||||
int64_t end = timeline.TimeStampUS();
|
||||
CopyBlobInfo(input_blob, output_blob);
|
||||
AddBlobInfo(output_blob, start);
|
||||
AddBlobInfo(output_blob, end);
|
||||
return 0;
|
||||
}
|
||||
|
||||
void mask_rcnn_r50_fpn_1x_coco::Resize(cv::Mat *img, float &scale_factor_h,
|
||||
float &scale_factor_w, int &im_shape_h,
|
||||
int &im_shape_w) {
|
||||
// keep_ratio
|
||||
int im_size_max = std::max(img->rows, img->cols);
|
||||
int im_size_min = std::min(img->rows, img->cols);
|
||||
int target_size_max = std::max(im_shape_h, im_shape_w);
|
||||
int target_size_min = std::min(im_shape_h, im_shape_w);
|
||||
float scale_min =
|
||||
static_cast<float>(target_size_min) / static_cast<float>(im_size_min);
|
||||
float scale_max =
|
||||
static_cast<float>(target_size_max) / static_cast<float>(im_size_max);
|
||||
float scale_ratio = std::min(scale_min, scale_max);
|
||||
|
||||
// scale_factor
|
||||
scale_factor_h = scale_ratio;
|
||||
scale_factor_w = scale_ratio;
|
||||
|
||||
// Resize
|
||||
cv::resize(*img, *img, cv::Size(), scale_ratio, scale_ratio, 2);
|
||||
im_shape_h = img->rows;
|
||||
im_shape_w = img->cols;
|
||||
}
|
||||
|
||||
void mask_rcnn_r50_fpn_1x_coco::Normalize(cv::Mat *img,
|
||||
const std::vector<float> &mean,
|
||||
const std::vector<float> &scale,
|
||||
const bool is_scale) {
|
||||
// Normalize
|
||||
double e = 1.0;
|
||||
if (is_scale) {
|
||||
e /= 255.0;
|
||||
}
|
||||
(*img).convertTo(*img, CV_32FC3, e);
|
||||
for (int h = 0; h < img->rows; h++) {
|
||||
for (int w = 0; w < img->cols; w++) {
|
||||
img->at<cv::Vec3f>(h, w)[0] =
|
||||
(img->at<cv::Vec3f>(h, w)[0] - mean[0]) / scale[0];
|
||||
img->at<cv::Vec3f>(h, w)[1] =
|
||||
(img->at<cv::Vec3f>(h, w)[1] - mean[1]) / scale[1];
|
||||
img->at<cv::Vec3f>(h, w)[2] =
|
||||
(img->at<cv::Vec3f>(h, w)[2] - mean[2]) / scale[2];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void mask_rcnn_r50_fpn_1x_coco::PadStride(cv::Mat *img, int stride_) {
|
||||
// PadStride
|
||||
if (stride_ <= 0)
|
||||
return;
|
||||
int rh = img->rows;
|
||||
int rw = img->cols;
|
||||
int nh = (rh / stride_) * stride_ + (rh % stride_ != 0) * stride_;
|
||||
int nw = (rw / stride_) * stride_ + (rw % stride_ != 0) * stride_;
|
||||
cv::copyMakeBorder(*img, *img, 0, nh - rh, 0, nw - rw, cv::BORDER_CONSTANT,
|
||||
cv::Scalar(0));
|
||||
}
|
||||
|
||||
void mask_rcnn_r50_fpn_1x_coco::Permute(const cv::Mat &img, float *data) {
|
||||
// Permute
|
||||
int rh = img.rows;
|
||||
int rw = img.cols;
|
||||
int rc = img.channels();
|
||||
for (int i = 0; i < rc; ++i) {
|
||||
cv::extractChannel(img, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw), i);
|
||||
}
|
||||
}
|
||||
|
||||
cv::Mat mask_rcnn_r50_fpn_1x_coco::Base2Mat(std::string &base64_data) {
|
||||
cv::Mat img;
|
||||
std::string s_mat;
|
||||
s_mat = base64Decode(base64_data.data(), base64_data.size());
|
||||
std::vector<char> base64_img(s_mat.begin(), s_mat.end());
|
||||
img = cv::imdecode(base64_img, cv::IMREAD_COLOR); // CV_LOAD_IMAGE_COLOR
|
||||
return img;
|
||||
}
|
||||
|
||||
std::string mask_rcnn_r50_fpn_1x_coco::base64Decode(const char *Data,
|
||||
int DataByte) {
|
||||
const char DecodeTable[] = {
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
62, // '+'
|
||||
0, 0, 0,
|
||||
63, // '/'
|
||||
52, 53, 54, 55, 56, 57, 58, 59, 60, 61, // '0'-'9'
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
|
||||
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, // 'A'-'Z'
|
||||
0, 0, 0, 0, 0, 0, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
|
||||
37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, // 'a'-'z'
|
||||
};
|
||||
|
||||
std::string strDecode;
|
||||
int nValue;
|
||||
int i = 0;
|
||||
while (i < DataByte) {
|
||||
if (*Data != '\r' && *Data != '\n') {
|
||||
nValue = DecodeTable[*Data++] << 18;
|
||||
nValue += DecodeTable[*Data++] << 12;
|
||||
strDecode += (nValue & 0x00FF0000) >> 16;
|
||||
if (*Data != '=') {
|
||||
nValue += DecodeTable[*Data++] << 6;
|
||||
strDecode += (nValue & 0x0000FF00) >> 8;
|
||||
if (*Data != '=') {
|
||||
nValue += DecodeTable[*Data++];
|
||||
strDecode += nValue & 0x000000FF;
|
||||
}
|
||||
}
|
||||
i += 4;
|
||||
} else // 回车换行,跳过
|
||||
{
|
||||
Data++;
|
||||
i++;
|
||||
}
|
||||
}
|
||||
return strDecode;
|
||||
}
|
||||
|
||||
DEFINE_OP(mask_rcnn_r50_fpn_1x_coco);
|
||||
|
||||
} // namespace serving
|
||||
} // namespace paddle_serving
|
||||
} // namespace baidu
|
||||
@@ -0,0 +1,72 @@
|
||||
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#pragma once
|
||||
#include "core/general-server/general_model_service.pb.h"
|
||||
#include "core/general-server/op/general_infer_helper.h"
|
||||
#include "paddle_inference_api.h" // NOLINT
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
#include "opencv2/core.hpp"
|
||||
#include "opencv2/imgcodecs.hpp"
|
||||
#include "opencv2/imgproc.hpp"
|
||||
#include <chrono>
|
||||
#include <iomanip>
|
||||
#include <iostream>
|
||||
#include <ostream>
|
||||
#include <vector>
|
||||
|
||||
#include <cstring>
|
||||
#include <fstream>
|
||||
#include <numeric>
|
||||
|
||||
namespace baidu {
|
||||
namespace paddle_serving {
|
||||
namespace serving {
|
||||
|
||||
class mask_rcnn_r50_fpn_1x_coco
|
||||
: public baidu::paddle_serving::predictor::OpWithChannel<GeneralBlob> {
|
||||
public:
|
||||
typedef std::vector<paddle::PaddleTensor> TensorVector;
|
||||
|
||||
DECLARE_OP(mask_rcnn_r50_fpn_1x_coco);
|
||||
|
||||
int inference();
|
||||
|
||||
private:
|
||||
// preprocess
|
||||
std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
|
||||
std::vector<float> scale_ = {0.229f, 0.224f, 0.225f};
|
||||
bool is_scale_ = true;
|
||||
int im_shape_h = 1333;
|
||||
int im_shape_w = 800;
|
||||
float scale_factor_h = 1.0f;
|
||||
float scale_factor_w = 1.0f;
|
||||
|
||||
void Resize(cv::Mat *img, float &scale_factor_h, float &scale_factor_w,
|
||||
int &im_shape_h, int &im_shape_w);
|
||||
void Normalize(cv::Mat *img, const std::vector<float> &mean,
|
||||
const std::vector<float> &scale, const bool is_scale);
|
||||
void PadStride(cv::Mat *img, int stride_ = -1);
|
||||
void Permute(const cv::Mat &img, float *data);
|
||||
|
||||
// read pics
|
||||
cv::Mat Base2Mat(std::string &base64_data);
|
||||
std::string base64Decode(const char *Data, int DataByte);
|
||||
};
|
||||
|
||||
} // namespace serving
|
||||
} // namespace paddle_serving
|
||||
} // namespace baidu
|
||||
@@ -0,0 +1,258 @@
|
||||
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#include "core/general-server/op/picodet_lcnet_1_5x_416_coco.h"
|
||||
#include "core/predictor/framework/infer.h"
|
||||
#include "core/predictor/framework/memory.h"
|
||||
#include "core/predictor/framework/resource.h"
|
||||
#include "core/util/include/timer.h"
|
||||
#include <algorithm>
|
||||
#include <iostream>
|
||||
#include <memory>
|
||||
#include <sstream>
|
||||
|
||||
namespace baidu {
|
||||
namespace paddle_serving {
|
||||
namespace serving {
|
||||
|
||||
using baidu::paddle_serving::Timer;
|
||||
using baidu::paddle_serving::predictor::InferManager;
|
||||
using baidu::paddle_serving::predictor::MempoolWrapper;
|
||||
using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
|
||||
using baidu::paddle_serving::predictor::general_model::Request;
|
||||
using baidu::paddle_serving::predictor::general_model::Response;
|
||||
using baidu::paddle_serving::predictor::general_model::Tensor;
|
||||
|
||||
int picodet_lcnet_1_5x_416_coco::inference() {
|
||||
VLOG(2) << "Going to run inference";
|
||||
const std::vector<std::string> pre_node_names = pre_names();
|
||||
if (pre_node_names.size() != 1) {
|
||||
LOG(ERROR) << "This op(" << op_name()
|
||||
<< ") can only have one predecessor op, but received "
|
||||
<< pre_node_names.size();
|
||||
return -1;
|
||||
}
|
||||
const std::string pre_name = pre_node_names[0];
|
||||
|
||||
const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name);
|
||||
if (!input_blob) {
|
||||
LOG(ERROR) << "input_blob is nullptr,error";
|
||||
return -1;
|
||||
}
|
||||
uint64_t log_id = input_blob->GetLogId();
|
||||
VLOG(2) << "(logid=" << log_id << ") Get precedent op name: " << pre_name;
|
||||
|
||||
GeneralBlob *output_blob = mutable_data<GeneralBlob>();
|
||||
if (!output_blob) {
|
||||
LOG(ERROR) << "output_blob is nullptr,error";
|
||||
return -1;
|
||||
}
|
||||
output_blob->SetLogId(log_id);
|
||||
|
||||
if (!input_blob) {
|
||||
LOG(ERROR) << "(logid=" << log_id
|
||||
<< ") Failed mutable depended argument, op:" << pre_name;
|
||||
return -1;
|
||||
}
|
||||
|
||||
const TensorVector *in = &input_blob->tensor_vector;
|
||||
TensorVector *out = &output_blob->tensor_vector;
|
||||
|
||||
int batch_size = input_blob->_batch_size;
|
||||
output_blob->_batch_size = batch_size;
|
||||
VLOG(2) << "(logid=" << log_id << ") infer batch size: " << batch_size;
|
||||
|
||||
Timer timeline;
|
||||
int64_t start = timeline.TimeStampUS();
|
||||
timeline.Start();
|
||||
|
||||
// only support string type
|
||||
char *total_input_ptr = static_cast<char *>(in->at(0).data.data());
|
||||
std::string base64str = total_input_ptr;
|
||||
|
||||
cv::Mat img = Base2Mat(base64str);
|
||||
cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
|
||||
|
||||
// preprocess
|
||||
std::vector<float> input(1 * 3 * im_shape_h * im_shape_w, 0.0f);
|
||||
preprocess_det(img, input.data(), scale_factor_h, scale_factor_w, im_shape_h,
|
||||
im_shape_w, mean_, scale_, is_scale_);
|
||||
|
||||
// create real_in
|
||||
TensorVector *real_in = new TensorVector();
|
||||
if (!real_in) {
|
||||
LOG(ERROR) << "real_in is nullptr,error";
|
||||
return -1;
|
||||
}
|
||||
|
||||
int in_num = 0;
|
||||
size_t databuf_size = 0;
|
||||
void *databuf_data = NULL;
|
||||
char *databuf_char = NULL;
|
||||
|
||||
// image
|
||||
in_num = 1 * 3 * im_shape_h * im_shape_w;
|
||||
databuf_size = in_num * sizeof(float);
|
||||
|
||||
databuf_data = MempoolWrapper::instance().malloc(databuf_size);
|
||||
if (!databuf_data) {
|
||||
LOG(ERROR) << "Malloc failed, size: " << databuf_size;
|
||||
return -1;
|
||||
}
|
||||
|
||||
memcpy(databuf_data, input.data(), databuf_size);
|
||||
databuf_char = reinterpret_cast<char *>(databuf_data);
|
||||
paddle::PaddleBuf paddleBuf(databuf_char, databuf_size);
|
||||
paddle::PaddleTensor tensor_in;
|
||||
tensor_in.name = "image";
|
||||
tensor_in.dtype = paddle::PaddleDType::FLOAT32;
|
||||
tensor_in.shape = {1, 3, im_shape_h, im_shape_w};
|
||||
tensor_in.lod = in->at(0).lod;
|
||||
tensor_in.data = paddleBuf;
|
||||
real_in->push_back(tensor_in);
|
||||
|
||||
// scale_factor
|
||||
std::vector<float> scale_factor{scale_factor_h, scale_factor_w};
|
||||
databuf_size = 2 * sizeof(float);
|
||||
|
||||
databuf_data = MempoolWrapper::instance().malloc(databuf_size);
|
||||
if (!databuf_data) {
|
||||
LOG(ERROR) << "Malloc failed, size: " << databuf_size;
|
||||
return -1;
|
||||
}
|
||||
|
||||
memcpy(databuf_data, scale_factor.data(), databuf_size);
|
||||
databuf_char = reinterpret_cast<char *>(databuf_data);
|
||||
paddle::PaddleBuf paddleBuf_2(databuf_char, databuf_size);
|
||||
paddle::PaddleTensor tensor_in_2;
|
||||
tensor_in_2.name = "scale_factor";
|
||||
tensor_in_2.dtype = paddle::PaddleDType::FLOAT32;
|
||||
tensor_in_2.shape = {1, 2};
|
||||
tensor_in_2.lod = in->at(0).lod;
|
||||
tensor_in_2.data = paddleBuf_2;
|
||||
real_in->push_back(tensor_in_2);
|
||||
|
||||
if (InferManager::instance().infer(engine_name().c_str(), real_in, out,
|
||||
batch_size)) {
|
||||
LOG(ERROR) << "(logid=" << log_id
|
||||
<< ") Failed do infer in fluid model: " << engine_name().c_str();
|
||||
return -1;
|
||||
}
|
||||
|
||||
int64_t end = timeline.TimeStampUS();
|
||||
CopyBlobInfo(input_blob, output_blob);
|
||||
AddBlobInfo(output_blob, start);
|
||||
AddBlobInfo(output_blob, end);
|
||||
return 0;
|
||||
}
|
||||
|
||||
void picodet_lcnet_1_5x_416_coco::preprocess_det(
|
||||
const cv::Mat &img, float *data, float &scale_factor_h,
|
||||
float &scale_factor_w, int im_shape_h, int im_shape_w,
|
||||
const std::vector<float> &mean, const std::vector<float> &scale,
|
||||
const bool is_scale) {
|
||||
// scale_factor
|
||||
scale_factor_h =
|
||||
static_cast<float>(im_shape_h) / static_cast<float>(img.rows);
|
||||
scale_factor_w =
|
||||
static_cast<float>(im_shape_w) / static_cast<float>(img.cols);
|
||||
|
||||
// Resize
|
||||
cv::Mat resize_img;
|
||||
cv::resize(img, resize_img, cv::Size(im_shape_w, im_shape_h), 0, 0, 2);
|
||||
|
||||
// Normalize
|
||||
double e = 1.0;
|
||||
if (is_scale) {
|
||||
e /= 255.0;
|
||||
}
|
||||
cv::Mat img_fp;
|
||||
(resize_img).convertTo(img_fp, CV_32FC3, e);
|
||||
for (int h = 0; h < im_shape_h; h++) {
|
||||
for (int w = 0; w < im_shape_w; w++) {
|
||||
img_fp.at<cv::Vec3f>(h, w)[0] =
|
||||
(img_fp.at<cv::Vec3f>(h, w)[0] - mean[0]) / scale[0];
|
||||
img_fp.at<cv::Vec3f>(h, w)[1] =
|
||||
(img_fp.at<cv::Vec3f>(h, w)[1] - mean[1]) / scale[1];
|
||||
img_fp.at<cv::Vec3f>(h, w)[2] =
|
||||
(img_fp.at<cv::Vec3f>(h, w)[2] - mean[2]) / scale[2];
|
||||
}
|
||||
}
|
||||
|
||||
// Permute
|
||||
int rh = img_fp.rows;
|
||||
int rw = img_fp.cols;
|
||||
int rc = img_fp.channels();
|
||||
for (int i = 0; i < rc; ++i) {
|
||||
cv::extractChannel(img_fp, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw),
|
||||
i);
|
||||
}
|
||||
}
|
||||
|
||||
cv::Mat picodet_lcnet_1_5x_416_coco::Base2Mat(std::string &base64_data) {
|
||||
cv::Mat img;
|
||||
std::string s_mat;
|
||||
s_mat = base64Decode(base64_data.data(), base64_data.size());
|
||||
std::vector<char> base64_img(s_mat.begin(), s_mat.end());
|
||||
img = cv::imdecode(base64_img, cv::IMREAD_COLOR); // CV_LOAD_IMAGE_COLOR
|
||||
return img;
|
||||
}
|
||||
|
||||
std::string picodet_lcnet_1_5x_416_coco::base64Decode(const char *Data,
|
||||
int DataByte) {
|
||||
const char DecodeTable[] = {
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
62, // '+'
|
||||
0, 0, 0,
|
||||
63, // '/'
|
||||
52, 53, 54, 55, 56, 57, 58, 59, 60, 61, // '0'-'9'
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
|
||||
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, // 'A'-'Z'
|
||||
0, 0, 0, 0, 0, 0, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
|
||||
37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, // 'a'-'z'
|
||||
};
|
||||
|
||||
std::string strDecode;
|
||||
int nValue;
|
||||
int i = 0;
|
||||
while (i < DataByte) {
|
||||
if (*Data != '\r' && *Data != '\n') {
|
||||
nValue = DecodeTable[*Data++] << 18;
|
||||
nValue += DecodeTable[*Data++] << 12;
|
||||
strDecode += (nValue & 0x00FF0000) >> 16;
|
||||
if (*Data != '=') {
|
||||
nValue += DecodeTable[*Data++] << 6;
|
||||
strDecode += (nValue & 0x0000FF00) >> 8;
|
||||
if (*Data != '=') {
|
||||
nValue += DecodeTable[*Data++];
|
||||
strDecode += nValue & 0x000000FF;
|
||||
}
|
||||
}
|
||||
i += 4;
|
||||
} else // 回车换行,跳过
|
||||
{
|
||||
Data++;
|
||||
i++;
|
||||
}
|
||||
}
|
||||
return strDecode;
|
||||
}
|
||||
|
||||
DEFINE_OP(picodet_lcnet_1_5x_416_coco);
|
||||
|
||||
} // namespace serving
|
||||
} // namespace paddle_serving
|
||||
} // namespace baidu
|
||||
@@ -0,0 +1,69 @@
|
||||
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#pragma once
|
||||
#include "core/general-server/general_model_service.pb.h"
|
||||
#include "core/general-server/op/general_infer_helper.h"
|
||||
#include "paddle_inference_api.h" // NOLINT
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
#include "opencv2/core.hpp"
|
||||
#include "opencv2/imgcodecs.hpp"
|
||||
#include "opencv2/imgproc.hpp"
|
||||
#include <chrono>
|
||||
#include <iomanip>
|
||||
#include <iostream>
|
||||
#include <ostream>
|
||||
#include <vector>
|
||||
|
||||
#include <cstring>
|
||||
#include <fstream>
|
||||
#include <numeric>
|
||||
|
||||
namespace baidu {
|
||||
namespace paddle_serving {
|
||||
namespace serving {
|
||||
|
||||
class picodet_lcnet_1_5x_416_coco
|
||||
: public baidu::paddle_serving::predictor::OpWithChannel<GeneralBlob> {
|
||||
public:
|
||||
typedef std::vector<paddle::PaddleTensor> TensorVector;
|
||||
|
||||
DECLARE_OP(picodet_lcnet_1_5x_416_coco);
|
||||
|
||||
int inference();
|
||||
|
||||
private:
|
||||
// preprocess
|
||||
std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
|
||||
std::vector<float> scale_ = {0.229f, 0.224f, 0.225f};
|
||||
bool is_scale_ = true;
|
||||
int im_shape_h = 416;
|
||||
int im_shape_w = 416;
|
||||
float scale_factor_h = 1.0f;
|
||||
float scale_factor_w = 1.0f;
|
||||
void preprocess_det(const cv::Mat &img, float *data, float &scale_factor_h,
|
||||
float &scale_factor_w, int im_shape_h, int im_shape_w,
|
||||
const std::vector<float> &mean,
|
||||
const std::vector<float> &scale, const bool is_scale);
|
||||
|
||||
// read pics
|
||||
cv::Mat Base2Mat(std::string &base64_data);
|
||||
std::string base64Decode(const char *Data, int DataByte);
|
||||
};
|
||||
|
||||
} // namespace serving
|
||||
} // namespace paddle_serving
|
||||
} // namespace baidu
|
||||
@@ -0,0 +1,282 @@
|
||||
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#include "core/general-server/op/ppyolo_mbv3_large_coco.h"
|
||||
#include "core/predictor/framework/infer.h"
|
||||
#include "core/predictor/framework/memory.h"
|
||||
#include "core/predictor/framework/resource.h"
|
||||
#include "core/util/include/timer.h"
|
||||
#include <algorithm>
|
||||
#include <iostream>
|
||||
#include <memory>
|
||||
#include <sstream>
|
||||
|
||||
namespace baidu {
|
||||
namespace paddle_serving {
|
||||
namespace serving {
|
||||
|
||||
using baidu::paddle_serving::Timer;
|
||||
using baidu::paddle_serving::predictor::InferManager;
|
||||
using baidu::paddle_serving::predictor::MempoolWrapper;
|
||||
using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
|
||||
using baidu::paddle_serving::predictor::general_model::Request;
|
||||
using baidu::paddle_serving::predictor::general_model::Response;
|
||||
using baidu::paddle_serving::predictor::general_model::Tensor;
|
||||
|
||||
int ppyolo_mbv3_large_coco::inference() {
|
||||
VLOG(2) << "Going to run inference";
|
||||
const std::vector<std::string> pre_node_names = pre_names();
|
||||
if (pre_node_names.size() != 1) {
|
||||
LOG(ERROR) << "This op(" << op_name()
|
||||
<< ") can only have one predecessor op, but received "
|
||||
<< pre_node_names.size();
|
||||
return -1;
|
||||
}
|
||||
const std::string pre_name = pre_node_names[0];
|
||||
|
||||
const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name);
|
||||
if (!input_blob) {
|
||||
LOG(ERROR) << "input_blob is nullptr,error";
|
||||
return -1;
|
||||
}
|
||||
uint64_t log_id = input_blob->GetLogId();
|
||||
VLOG(2) << "(logid=" << log_id << ") Get precedent op name: " << pre_name;
|
||||
|
||||
GeneralBlob *output_blob = mutable_data<GeneralBlob>();
|
||||
if (!output_blob) {
|
||||
LOG(ERROR) << "output_blob is nullptr,error";
|
||||
return -1;
|
||||
}
|
||||
output_blob->SetLogId(log_id);
|
||||
|
||||
if (!input_blob) {
|
||||
LOG(ERROR) << "(logid=" << log_id
|
||||
<< ") Failed mutable depended argument, op:" << pre_name;
|
||||
return -1;
|
||||
}
|
||||
|
||||
const TensorVector *in = &input_blob->tensor_vector;
|
||||
TensorVector *out = &output_blob->tensor_vector;
|
||||
|
||||
int batch_size = input_blob->_batch_size;
|
||||
output_blob->_batch_size = batch_size;
|
||||
VLOG(2) << "(logid=" << log_id << ") infer batch size: " << batch_size;
|
||||
|
||||
Timer timeline;
|
||||
int64_t start = timeline.TimeStampUS();
|
||||
timeline.Start();
|
||||
|
||||
// only support string type
|
||||
char *total_input_ptr = static_cast<char *>(in->at(0).data.data());
|
||||
std::string base64str = total_input_ptr;
|
||||
|
||||
cv::Mat img = Base2Mat(base64str);
|
||||
cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
|
||||
|
||||
// preprocess
|
||||
std::vector<float> input(1 * 3 * im_shape_h * im_shape_w, 0.0f);
|
||||
preprocess_det(img, input.data(), scale_factor_h, scale_factor_w, im_shape_h,
|
||||
im_shape_w, mean_, scale_, is_scale_);
|
||||
|
||||
// create real_in
|
||||
TensorVector *real_in = new TensorVector();
|
||||
if (!real_in) {
|
||||
LOG(ERROR) << "real_in is nullptr,error";
|
||||
return -1;
|
||||
}
|
||||
|
||||
int in_num = 0;
|
||||
size_t databuf_size = 0;
|
||||
void *databuf_data = NULL;
|
||||
char *databuf_char = NULL;
|
||||
|
||||
// im_shape
|
||||
std::vector<float> im_shape{static_cast<float>(im_shape_h),
|
||||
static_cast<float>(im_shape_w)};
|
||||
databuf_size = 2 * sizeof(float);
|
||||
|
||||
databuf_data = MempoolWrapper::instance().malloc(databuf_size);
|
||||
if (!databuf_data) {
|
||||
LOG(ERROR) << "Malloc failed, size: " << databuf_size;
|
||||
return -1;
|
||||
}
|
||||
|
||||
memcpy(databuf_data, im_shape.data(), databuf_size);
|
||||
databuf_char = reinterpret_cast<char *>(databuf_data);
|
||||
paddle::PaddleBuf paddleBuf_0(databuf_char, databuf_size);
|
||||
paddle::PaddleTensor tensor_in_0;
|
||||
tensor_in_0.name = "im_shape";
|
||||
tensor_in_0.dtype = paddle::PaddleDType::FLOAT32;
|
||||
tensor_in_0.shape = {1, 2};
|
||||
tensor_in_0.lod = in->at(0).lod;
|
||||
tensor_in_0.data = paddleBuf_0;
|
||||
real_in->push_back(tensor_in_0);
|
||||
|
||||
// image
|
||||
in_num = 1 * 3 * im_shape_h * im_shape_w;
|
||||
databuf_size = in_num * sizeof(float);
|
||||
|
||||
databuf_data = MempoolWrapper::instance().malloc(databuf_size);
|
||||
if (!databuf_data) {
|
||||
LOG(ERROR) << "Malloc failed, size: " << databuf_size;
|
||||
return -1;
|
||||
}
|
||||
|
||||
memcpy(databuf_data, input.data(), databuf_size);
|
||||
databuf_char = reinterpret_cast<char *>(databuf_data);
|
||||
paddle::PaddleBuf paddleBuf_1(databuf_char, databuf_size);
|
||||
paddle::PaddleTensor tensor_in_1;
|
||||
tensor_in_1.name = "image";
|
||||
tensor_in_1.dtype = paddle::PaddleDType::FLOAT32;
|
||||
tensor_in_1.shape = {1, 3, im_shape_h, im_shape_w};
|
||||
tensor_in_1.lod = in->at(0).lod;
|
||||
tensor_in_1.data = paddleBuf_1;
|
||||
real_in->push_back(tensor_in_1);
|
||||
|
||||
// scale_factor
|
||||
std::vector<float> scale_factor{scale_factor_h, scale_factor_w};
|
||||
databuf_size = 2 * sizeof(float);
|
||||
|
||||
databuf_data = MempoolWrapper::instance().malloc(databuf_size);
|
||||
if (!databuf_data) {
|
||||
LOG(ERROR) << "Malloc failed, size: " << databuf_size;
|
||||
return -1;
|
||||
}
|
||||
|
||||
memcpy(databuf_data, scale_factor.data(), databuf_size);
|
||||
databuf_char = reinterpret_cast<char *>(databuf_data);
|
||||
paddle::PaddleBuf paddleBuf_2(databuf_char, databuf_size);
|
||||
paddle::PaddleTensor tensor_in_2;
|
||||
tensor_in_2.name = "scale_factor";
|
||||
tensor_in_2.dtype = paddle::PaddleDType::FLOAT32;
|
||||
tensor_in_2.shape = {1, 2};
|
||||
tensor_in_2.lod = in->at(0).lod;
|
||||
tensor_in_2.data = paddleBuf_2;
|
||||
real_in->push_back(tensor_in_2);
|
||||
|
||||
if (InferManager::instance().infer(engine_name().c_str(), real_in, out,
|
||||
batch_size)) {
|
||||
LOG(ERROR) << "(logid=" << log_id
|
||||
<< ") Failed do infer in fluid model: " << engine_name().c_str();
|
||||
return -1;
|
||||
}
|
||||
|
||||
int64_t end = timeline.TimeStampUS();
|
||||
CopyBlobInfo(input_blob, output_blob);
|
||||
AddBlobInfo(output_blob, start);
|
||||
AddBlobInfo(output_blob, end);
|
||||
return 0;
|
||||
}
|
||||
|
||||
void ppyolo_mbv3_large_coco::preprocess_det(const cv::Mat &img, float *data,
|
||||
float &scale_factor_h,
|
||||
float &scale_factor_w,
|
||||
int im_shape_h, int im_shape_w,
|
||||
const std::vector<float> &mean,
|
||||
const std::vector<float> &scale,
|
||||
const bool is_scale) {
|
||||
// scale_factor
|
||||
scale_factor_h =
|
||||
static_cast<float>(im_shape_h) / static_cast<float>(img.rows);
|
||||
scale_factor_w =
|
||||
static_cast<float>(im_shape_w) / static_cast<float>(img.cols);
|
||||
|
||||
// Resize
|
||||
cv::Mat resize_img;
|
||||
cv::resize(img, resize_img, cv::Size(im_shape_w, im_shape_h), 0, 0, 2);
|
||||
|
||||
// Normalize
|
||||
double e = 1.0;
|
||||
if (is_scale) {
|
||||
e /= 255.0;
|
||||
}
|
||||
cv::Mat img_fp;
|
||||
(resize_img).convertTo(img_fp, CV_32FC3, e);
|
||||
for (int h = 0; h < im_shape_h; h++) {
|
||||
for (int w = 0; w < im_shape_w; w++) {
|
||||
img_fp.at<cv::Vec3f>(h, w)[0] =
|
||||
(img_fp.at<cv::Vec3f>(h, w)[0] - mean[0]) / scale[0];
|
||||
img_fp.at<cv::Vec3f>(h, w)[1] =
|
||||
(img_fp.at<cv::Vec3f>(h, w)[1] - mean[1]) / scale[1];
|
||||
img_fp.at<cv::Vec3f>(h, w)[2] =
|
||||
(img_fp.at<cv::Vec3f>(h, w)[2] - mean[2]) / scale[2];
|
||||
}
|
||||
}
|
||||
|
||||
// Permute
|
||||
int rh = img_fp.rows;
|
||||
int rw = img_fp.cols;
|
||||
int rc = img_fp.channels();
|
||||
for (int i = 0; i < rc; ++i) {
|
||||
cv::extractChannel(img_fp, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw),
|
||||
i);
|
||||
}
|
||||
}
|
||||
|
||||
cv::Mat ppyolo_mbv3_large_coco::Base2Mat(std::string &base64_data) {
|
||||
cv::Mat img;
|
||||
std::string s_mat;
|
||||
s_mat = base64Decode(base64_data.data(), base64_data.size());
|
||||
std::vector<char> base64_img(s_mat.begin(), s_mat.end());
|
||||
img = cv::imdecode(base64_img, cv::IMREAD_COLOR); // CV_LOAD_IMAGE_COLOR
|
||||
return img;
|
||||
}
|
||||
|
||||
std::string ppyolo_mbv3_large_coco::base64Decode(const char *Data,
|
||||
int DataByte) {
|
||||
const char DecodeTable[] = {
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
62, // '+'
|
||||
0, 0, 0,
|
||||
63, // '/'
|
||||
52, 53, 54, 55, 56, 57, 58, 59, 60, 61, // '0'-'9'
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
|
||||
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, // 'A'-'Z'
|
||||
0, 0, 0, 0, 0, 0, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
|
||||
37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, // 'a'-'z'
|
||||
};
|
||||
|
||||
std::string strDecode;
|
||||
int nValue;
|
||||
int i = 0;
|
||||
while (i < DataByte) {
|
||||
if (*Data != '\r' && *Data != '\n') {
|
||||
nValue = DecodeTable[*Data++] << 18;
|
||||
nValue += DecodeTable[*Data++] << 12;
|
||||
strDecode += (nValue & 0x00FF0000) >> 16;
|
||||
if (*Data != '=') {
|
||||
nValue += DecodeTable[*Data++] << 6;
|
||||
strDecode += (nValue & 0x0000FF00) >> 8;
|
||||
if (*Data != '=') {
|
||||
nValue += DecodeTable[*Data++];
|
||||
strDecode += nValue & 0x000000FF;
|
||||
}
|
||||
}
|
||||
i += 4;
|
||||
} else // 回车换行,跳过
|
||||
{
|
||||
Data++;
|
||||
i++;
|
||||
}
|
||||
}
|
||||
return strDecode;
|
||||
}
|
||||
|
||||
DEFINE_OP(ppyolo_mbv3_large_coco);
|
||||
|
||||
} // namespace serving
|
||||
} // namespace paddle_serving
|
||||
} // namespace baidu
|
||||
@@ -0,0 +1,69 @@
|
||||
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#pragma once
|
||||
#include "core/general-server/general_model_service.pb.h"
|
||||
#include "core/general-server/op/general_infer_helper.h"
|
||||
#include "paddle_inference_api.h" // NOLINT
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
#include "opencv2/core.hpp"
|
||||
#include "opencv2/imgcodecs.hpp"
|
||||
#include "opencv2/imgproc.hpp"
|
||||
#include <chrono>
|
||||
#include <iomanip>
|
||||
#include <iostream>
|
||||
#include <ostream>
|
||||
#include <vector>
|
||||
|
||||
#include <cstring>
|
||||
#include <fstream>
|
||||
#include <numeric>
|
||||
|
||||
namespace baidu {
|
||||
namespace paddle_serving {
|
||||
namespace serving {
|
||||
|
||||
class ppyolo_mbv3_large_coco
|
||||
: public baidu::paddle_serving::predictor::OpWithChannel<GeneralBlob> {
|
||||
public:
|
||||
typedef std::vector<paddle::PaddleTensor> TensorVector;
|
||||
|
||||
DECLARE_OP(ppyolo_mbv3_large_coco);
|
||||
|
||||
int inference();
|
||||
|
||||
private:
|
||||
// preprocess
|
||||
std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
|
||||
std::vector<float> scale_ = {0.229f, 0.224f, 0.225f};
|
||||
bool is_scale_ = true;
|
||||
int im_shape_h = 320;
|
||||
int im_shape_w = 320;
|
||||
float scale_factor_h = 1.0f;
|
||||
float scale_factor_w = 1.0f;
|
||||
void preprocess_det(const cv::Mat &img, float *data, float &scale_factor_h,
|
||||
float &scale_factor_w, int im_shape_h, int im_shape_w,
|
||||
const std::vector<float> &mean,
|
||||
const std::vector<float> &scale, const bool is_scale);
|
||||
|
||||
// read pics
|
||||
cv::Mat Base2Mat(std::string &base64_data);
|
||||
std::string base64Decode(const char *Data, int DataByte);
|
||||
};
|
||||
|
||||
} // namespace serving
|
||||
} // namespace paddle_serving
|
||||
} // namespace baidu
|
||||
@@ -0,0 +1,260 @@
|
||||
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#include "core/general-server/op/ppyoloe_crn_s_300e_coco.h"
|
||||
#include "core/predictor/framework/infer.h"
|
||||
#include "core/predictor/framework/memory.h"
|
||||
#include "core/predictor/framework/resource.h"
|
||||
#include "core/util/include/timer.h"
|
||||
#include <algorithm>
|
||||
#include <iostream>
|
||||
#include <memory>
|
||||
#include <sstream>
|
||||
|
||||
namespace baidu {
|
||||
namespace paddle_serving {
|
||||
namespace serving {
|
||||
|
||||
using baidu::paddle_serving::Timer;
|
||||
using baidu::paddle_serving::predictor::InferManager;
|
||||
using baidu::paddle_serving::predictor::MempoolWrapper;
|
||||
using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
|
||||
using baidu::paddle_serving::predictor::general_model::Request;
|
||||
using baidu::paddle_serving::predictor::general_model::Response;
|
||||
using baidu::paddle_serving::predictor::general_model::Tensor;
|
||||
|
||||
int ppyoloe_crn_s_300e_coco::inference() {
|
||||
VLOG(2) << "Going to run inference";
|
||||
const std::vector<std::string> pre_node_names = pre_names();
|
||||
if (pre_node_names.size() != 1) {
|
||||
LOG(ERROR) << "This op(" << op_name()
|
||||
<< ") can only have one predecessor op, but received "
|
||||
<< pre_node_names.size();
|
||||
return -1;
|
||||
}
|
||||
const std::string pre_name = pre_node_names[0];
|
||||
|
||||
const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name);
|
||||
if (!input_blob) {
|
||||
LOG(ERROR) << "input_blob is nullptr,error";
|
||||
return -1;
|
||||
}
|
||||
uint64_t log_id = input_blob->GetLogId();
|
||||
VLOG(2) << "(logid=" << log_id << ") Get precedent op name: " << pre_name;
|
||||
|
||||
GeneralBlob *output_blob = mutable_data<GeneralBlob>();
|
||||
if (!output_blob) {
|
||||
LOG(ERROR) << "output_blob is nullptr,error";
|
||||
return -1;
|
||||
}
|
||||
output_blob->SetLogId(log_id);
|
||||
|
||||
if (!input_blob) {
|
||||
LOG(ERROR) << "(logid=" << log_id
|
||||
<< ") Failed mutable depended argument, op:" << pre_name;
|
||||
return -1;
|
||||
}
|
||||
|
||||
const TensorVector *in = &input_blob->tensor_vector;
|
||||
TensorVector *out = &output_blob->tensor_vector;
|
||||
|
||||
int batch_size = input_blob->_batch_size;
|
||||
output_blob->_batch_size = batch_size;
|
||||
VLOG(2) << "(logid=" << log_id << ") infer batch size: " << batch_size;
|
||||
|
||||
Timer timeline;
|
||||
int64_t start = timeline.TimeStampUS();
|
||||
timeline.Start();
|
||||
|
||||
// only support string type
|
||||
char *total_input_ptr = static_cast<char *>(in->at(0).data.data());
|
||||
std::string base64str = total_input_ptr;
|
||||
|
||||
cv::Mat img = Base2Mat(base64str);
|
||||
cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
|
||||
|
||||
// preprocess
|
||||
std::vector<float> input(1 * 3 * im_shape_h * im_shape_w, 0.0f);
|
||||
preprocess_det(img, input.data(), scale_factor_h, scale_factor_w, im_shape_h,
|
||||
im_shape_w, mean_, scale_, is_scale_);
|
||||
|
||||
// create real_in
|
||||
TensorVector *real_in = new TensorVector();
|
||||
if (!real_in) {
|
||||
LOG(ERROR) << "real_in is nullptr,error";
|
||||
return -1;
|
||||
}
|
||||
|
||||
int in_num = 0;
|
||||
size_t databuf_size = 0;
|
||||
void *databuf_data = NULL;
|
||||
char *databuf_char = NULL;
|
||||
|
||||
// image
|
||||
in_num = 1 * 3 * im_shape_h * im_shape_w;
|
||||
databuf_size = in_num * sizeof(float);
|
||||
|
||||
databuf_data = MempoolWrapper::instance().malloc(databuf_size);
|
||||
if (!databuf_data) {
|
||||
LOG(ERROR) << "Malloc failed, size: " << databuf_size;
|
||||
return -1;
|
||||
}
|
||||
|
||||
memcpy(databuf_data, input.data(), databuf_size);
|
||||
databuf_char = reinterpret_cast<char *>(databuf_data);
|
||||
paddle::PaddleBuf paddleBuf(databuf_char, databuf_size);
|
||||
paddle::PaddleTensor tensor_in;
|
||||
tensor_in.name = "image";
|
||||
tensor_in.dtype = paddle::PaddleDType::FLOAT32;
|
||||
tensor_in.shape = {1, 3, im_shape_h, im_shape_w};
|
||||
tensor_in.lod = in->at(0).lod;
|
||||
tensor_in.data = paddleBuf;
|
||||
real_in->push_back(tensor_in);
|
||||
|
||||
// scale_factor
|
||||
std::vector<float> scale_factor{scale_factor_h, scale_factor_w};
|
||||
databuf_size = 2 * sizeof(float);
|
||||
|
||||
databuf_data = MempoolWrapper::instance().malloc(databuf_size);
|
||||
if (!databuf_data) {
|
||||
LOG(ERROR) << "Malloc failed, size: " << databuf_size;
|
||||
return -1;
|
||||
}
|
||||
|
||||
memcpy(databuf_data, scale_factor.data(), databuf_size);
|
||||
databuf_char = reinterpret_cast<char *>(databuf_data);
|
||||
paddle::PaddleBuf paddleBuf_2(databuf_char, databuf_size);
|
||||
paddle::PaddleTensor tensor_in_2;
|
||||
tensor_in_2.name = "scale_factor";
|
||||
tensor_in_2.dtype = paddle::PaddleDType::FLOAT32;
|
||||
tensor_in_2.shape = {1, 2};
|
||||
tensor_in_2.lod = in->at(0).lod;
|
||||
tensor_in_2.data = paddleBuf_2;
|
||||
real_in->push_back(tensor_in_2);
|
||||
|
||||
if (InferManager::instance().infer(engine_name().c_str(), real_in, out,
|
||||
batch_size)) {
|
||||
LOG(ERROR) << "(logid=" << log_id
|
||||
<< ") Failed do infer in fluid model: " << engine_name().c_str();
|
||||
return -1;
|
||||
}
|
||||
|
||||
int64_t end = timeline.TimeStampUS();
|
||||
CopyBlobInfo(input_blob, output_blob);
|
||||
AddBlobInfo(output_blob, start);
|
||||
AddBlobInfo(output_blob, end);
|
||||
return 0;
|
||||
}
|
||||
|
||||
void ppyoloe_crn_s_300e_coco::preprocess_det(const cv::Mat &img, float *data,
|
||||
float &scale_factor_h,
|
||||
float &scale_factor_w,
|
||||
int im_shape_h, int im_shape_w,
|
||||
const std::vector<float> &mean,
|
||||
const std::vector<float> &scale,
|
||||
const bool is_scale) {
|
||||
// scale_factor
|
||||
scale_factor_h =
|
||||
static_cast<float>(im_shape_h) / static_cast<float>(img.rows);
|
||||
scale_factor_w =
|
||||
static_cast<float>(im_shape_w) / static_cast<float>(img.cols);
|
||||
|
||||
// Resize
|
||||
cv::Mat resize_img;
|
||||
cv::resize(img, resize_img, cv::Size(im_shape_w, im_shape_h), 0, 0, 2);
|
||||
|
||||
// Normalize
|
||||
double e = 1.0;
|
||||
if (is_scale) {
|
||||
e /= 255.0;
|
||||
}
|
||||
cv::Mat img_fp;
|
||||
(resize_img).convertTo(img_fp, CV_32FC3, e);
|
||||
for (int h = 0; h < im_shape_h; h++) {
|
||||
for (int w = 0; w < im_shape_w; w++) {
|
||||
img_fp.at<cv::Vec3f>(h, w)[0] =
|
||||
(img_fp.at<cv::Vec3f>(h, w)[0] - mean[0]) / scale[0];
|
||||
img_fp.at<cv::Vec3f>(h, w)[1] =
|
||||
(img_fp.at<cv::Vec3f>(h, w)[1] - mean[1]) / scale[1];
|
||||
img_fp.at<cv::Vec3f>(h, w)[2] =
|
||||
(img_fp.at<cv::Vec3f>(h, w)[2] - mean[2]) / scale[2];
|
||||
}
|
||||
}
|
||||
|
||||
// Permute
|
||||
int rh = img_fp.rows;
|
||||
int rw = img_fp.cols;
|
||||
int rc = img_fp.channels();
|
||||
for (int i = 0; i < rc; ++i) {
|
||||
cv::extractChannel(img_fp, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw),
|
||||
i);
|
||||
}
|
||||
}
|
||||
|
||||
cv::Mat ppyoloe_crn_s_300e_coco::Base2Mat(std::string &base64_data) {
|
||||
cv::Mat img;
|
||||
std::string s_mat;
|
||||
s_mat = base64Decode(base64_data.data(), base64_data.size());
|
||||
std::vector<char> base64_img(s_mat.begin(), s_mat.end());
|
||||
img = cv::imdecode(base64_img, cv::IMREAD_COLOR); // CV_LOAD_IMAGE_COLOR
|
||||
return img;
|
||||
}
|
||||
|
||||
std::string ppyoloe_crn_s_300e_coco::base64Decode(const char *Data,
|
||||
int DataByte) {
|
||||
const char DecodeTable[] = {
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
62, // '+'
|
||||
0, 0, 0,
|
||||
63, // '/'
|
||||
52, 53, 54, 55, 56, 57, 58, 59, 60, 61, // '0'-'9'
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
|
||||
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, // 'A'-'Z'
|
||||
0, 0, 0, 0, 0, 0, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
|
||||
37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, // 'a'-'z'
|
||||
};
|
||||
|
||||
std::string strDecode;
|
||||
int nValue;
|
||||
int i = 0;
|
||||
while (i < DataByte) {
|
||||
if (*Data != '\r' && *Data != '\n') {
|
||||
nValue = DecodeTable[*Data++] << 18;
|
||||
nValue += DecodeTable[*Data++] << 12;
|
||||
strDecode += (nValue & 0x00FF0000) >> 16;
|
||||
if (*Data != '=') {
|
||||
nValue += DecodeTable[*Data++] << 6;
|
||||
strDecode += (nValue & 0x0000FF00) >> 8;
|
||||
if (*Data != '=') {
|
||||
nValue += DecodeTable[*Data++];
|
||||
strDecode += nValue & 0x000000FF;
|
||||
}
|
||||
}
|
||||
i += 4;
|
||||
} else // 回车换行,跳过
|
||||
{
|
||||
Data++;
|
||||
i++;
|
||||
}
|
||||
}
|
||||
return strDecode;
|
||||
}
|
||||
|
||||
DEFINE_OP(ppyoloe_crn_s_300e_coco);
|
||||
|
||||
} // namespace serving
|
||||
} // namespace paddle_serving
|
||||
} // namespace baidu
|
||||
@@ -0,0 +1,69 @@
|
||||
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#pragma once
|
||||
#include "core/general-server/general_model_service.pb.h"
|
||||
#include "core/general-server/op/general_infer_helper.h"
|
||||
#include "paddle_inference_api.h" // NOLINT
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
#include "opencv2/core.hpp"
|
||||
#include "opencv2/imgcodecs.hpp"
|
||||
#include "opencv2/imgproc.hpp"
|
||||
#include <chrono>
|
||||
#include <iomanip>
|
||||
#include <iostream>
|
||||
#include <ostream>
|
||||
#include <vector>
|
||||
|
||||
#include <cstring>
|
||||
#include <fstream>
|
||||
#include <numeric>
|
||||
|
||||
namespace baidu {
|
||||
namespace paddle_serving {
|
||||
namespace serving {
|
||||
|
||||
class ppyoloe_crn_s_300e_coco
|
||||
: public baidu::paddle_serving::predictor::OpWithChannel<GeneralBlob> {
|
||||
public:
|
||||
typedef std::vector<paddle::PaddleTensor> TensorVector;
|
||||
|
||||
DECLARE_OP(ppyoloe_crn_s_300e_coco);
|
||||
|
||||
int inference();
|
||||
|
||||
private:
|
||||
// preprocess
|
||||
std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
|
||||
std::vector<float> scale_ = {0.229f, 0.224f, 0.225f};
|
||||
bool is_scale_ = true;
|
||||
int im_shape_h = 640;
|
||||
int im_shape_w = 640;
|
||||
float scale_factor_h = 1.0f;
|
||||
float scale_factor_w = 1.0f;
|
||||
void preprocess_det(const cv::Mat &img, float *data, float &scale_factor_h,
|
||||
float &scale_factor_w, int im_shape_h, int im_shape_w,
|
||||
const std::vector<float> &mean,
|
||||
const std::vector<float> &scale, const bool is_scale);
|
||||
|
||||
// read pics
|
||||
cv::Mat Base2Mat(std::string &base64_data);
|
||||
std::string base64Decode(const char *Data, int DataByte);
|
||||
};
|
||||
|
||||
} // namespace serving
|
||||
} // namespace paddle_serving
|
||||
} // namespace baidu
|
||||
@@ -0,0 +1,232 @@
|
||||
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#include "core/general-server/op/tinypose_128x96.h"
|
||||
#include "core/predictor/framework/infer.h"
|
||||
#include "core/predictor/framework/memory.h"
|
||||
#include "core/predictor/framework/resource.h"
|
||||
#include "core/util/include/timer.h"
|
||||
#include <algorithm>
|
||||
#include <iostream>
|
||||
#include <memory>
|
||||
#include <sstream>
|
||||
|
||||
namespace baidu {
|
||||
namespace paddle_serving {
|
||||
namespace serving {
|
||||
|
||||
using baidu::paddle_serving::Timer;
|
||||
using baidu::paddle_serving::predictor::InferManager;
|
||||
using baidu::paddle_serving::predictor::MempoolWrapper;
|
||||
using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
|
||||
using baidu::paddle_serving::predictor::general_model::Request;
|
||||
using baidu::paddle_serving::predictor::general_model::Response;
|
||||
using baidu::paddle_serving::predictor::general_model::Tensor;
|
||||
|
||||
int tinypose_128x96::inference() {
|
||||
VLOG(2) << "Going to run inference";
|
||||
const std::vector<std::string> pre_node_names = pre_names();
|
||||
if (pre_node_names.size() != 1) {
|
||||
LOG(ERROR) << "This op(" << op_name()
|
||||
<< ") can only have one predecessor op, but received "
|
||||
<< pre_node_names.size();
|
||||
return -1;
|
||||
}
|
||||
const std::string pre_name = pre_node_names[0];
|
||||
|
||||
const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name);
|
||||
if (!input_blob) {
|
||||
LOG(ERROR) << "input_blob is nullptr,error";
|
||||
return -1;
|
||||
}
|
||||
uint64_t log_id = input_blob->GetLogId();
|
||||
VLOG(2) << "(logid=" << log_id << ") Get precedent op name: " << pre_name;
|
||||
|
||||
GeneralBlob *output_blob = mutable_data<GeneralBlob>();
|
||||
if (!output_blob) {
|
||||
LOG(ERROR) << "output_blob is nullptr,error";
|
||||
return -1;
|
||||
}
|
||||
output_blob->SetLogId(log_id);
|
||||
|
||||
if (!input_blob) {
|
||||
LOG(ERROR) << "(logid=" << log_id
|
||||
<< ") Failed mutable depended argument, op:" << pre_name;
|
||||
return -1;
|
||||
}
|
||||
|
||||
const TensorVector *in = &input_blob->tensor_vector;
|
||||
TensorVector *out = &output_blob->tensor_vector;
|
||||
|
||||
int batch_size = input_blob->_batch_size;
|
||||
output_blob->_batch_size = batch_size;
|
||||
VLOG(2) << "(logid=" << log_id << ") infer batch size: " << batch_size;
|
||||
|
||||
Timer timeline;
|
||||
int64_t start = timeline.TimeStampUS();
|
||||
timeline.Start();
|
||||
|
||||
// only support string type
|
||||
char *total_input_ptr = static_cast<char *>(in->at(0).data.data());
|
||||
std::string base64str = total_input_ptr;
|
||||
|
||||
cv::Mat img = Base2Mat(base64str);
|
||||
cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
|
||||
|
||||
// preprocess
|
||||
std::vector<float> input(1 * 3 * im_shape_h * im_shape_w, 0.0f);
|
||||
preprocess_det(img, input.data(), scale_factor_h, scale_factor_w, im_shape_h,
|
||||
im_shape_w, mean_, scale_, is_scale_);
|
||||
|
||||
// create real_in
|
||||
TensorVector *real_in = new TensorVector();
|
||||
if (!real_in) {
|
||||
LOG(ERROR) << "real_in is nullptr,error";
|
||||
return -1;
|
||||
}
|
||||
|
||||
int in_num = 0;
|
||||
size_t databuf_size = 0;
|
||||
void *databuf_data = NULL;
|
||||
char *databuf_char = NULL;
|
||||
|
||||
// image
|
||||
in_num = 1 * 3 * im_shape_h * im_shape_w;
|
||||
databuf_size = in_num * sizeof(float);
|
||||
|
||||
databuf_data = MempoolWrapper::instance().malloc(databuf_size);
|
||||
if (!databuf_data) {
|
||||
LOG(ERROR) << "Malloc failed, size: " << databuf_size;
|
||||
return -1;
|
||||
}
|
||||
|
||||
memcpy(databuf_data, input.data(), databuf_size);
|
||||
databuf_char = reinterpret_cast<char *>(databuf_data);
|
||||
paddle::PaddleBuf paddleBuf(databuf_char, databuf_size);
|
||||
paddle::PaddleTensor tensor_in;
|
||||
tensor_in.name = "image";
|
||||
tensor_in.dtype = paddle::PaddleDType::FLOAT32;
|
||||
tensor_in.shape = {1, 3, im_shape_h, im_shape_w};
|
||||
tensor_in.lod = in->at(0).lod;
|
||||
tensor_in.data = paddleBuf;
|
||||
real_in->push_back(tensor_in);
|
||||
|
||||
if (InferManager::instance().infer(engine_name().c_str(), real_in, out,
|
||||
batch_size)) {
|
||||
LOG(ERROR) << "(logid=" << log_id
|
||||
<< ") Failed do infer in fluid model: " << engine_name().c_str();
|
||||
return -1;
|
||||
}
|
||||
|
||||
int64_t end = timeline.TimeStampUS();
|
||||
CopyBlobInfo(input_blob, output_blob);
|
||||
AddBlobInfo(output_blob, start);
|
||||
AddBlobInfo(output_blob, end);
|
||||
return 0;
|
||||
}
|
||||
|
||||
void tinypose_128x96::preprocess_det(const cv::Mat &img, float *data,
|
||||
float &scale_factor_h,
|
||||
float &scale_factor_w, int im_shape_h,
|
||||
int im_shape_w,
|
||||
const std::vector<float> &mean,
|
||||
const std::vector<float> &scale,
|
||||
const bool is_scale) {
|
||||
// Resize
|
||||
cv::Mat resize_img;
|
||||
cv::resize(img, resize_img, cv::Size(im_shape_w, im_shape_h), 0, 0, 1);
|
||||
|
||||
// Normalize
|
||||
double e = 1.0;
|
||||
if (is_scale) {
|
||||
e /= 255.0;
|
||||
}
|
||||
cv::Mat img_fp;
|
||||
(resize_img).convertTo(img_fp, CV_32FC3, e);
|
||||
for (int h = 0; h < im_shape_h; h++) {
|
||||
for (int w = 0; w < im_shape_w; w++) {
|
||||
img_fp.at<cv::Vec3f>(h, w)[0] =
|
||||
(img_fp.at<cv::Vec3f>(h, w)[0] - mean[0]) / scale[0];
|
||||
img_fp.at<cv::Vec3f>(h, w)[1] =
|
||||
(img_fp.at<cv::Vec3f>(h, w)[1] - mean[1]) / scale[1];
|
||||
img_fp.at<cv::Vec3f>(h, w)[2] =
|
||||
(img_fp.at<cv::Vec3f>(h, w)[2] - mean[2]) / scale[2];
|
||||
}
|
||||
}
|
||||
|
||||
// Permute
|
||||
int rh = img_fp.rows;
|
||||
int rw = img_fp.cols;
|
||||
int rc = img_fp.channels();
|
||||
for (int i = 0; i < rc; ++i) {
|
||||
cv::extractChannel(img_fp, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw),
|
||||
i);
|
||||
}
|
||||
}
|
||||
|
||||
cv::Mat tinypose_128x96::Base2Mat(std::string &base64_data) {
|
||||
cv::Mat img;
|
||||
std::string s_mat;
|
||||
s_mat = base64Decode(base64_data.data(), base64_data.size());
|
||||
std::vector<char> base64_img(s_mat.begin(), s_mat.end());
|
||||
img = cv::imdecode(base64_img, cv::IMREAD_COLOR); // CV_LOAD_IMAGE_COLOR
|
||||
return img;
|
||||
}
|
||||
|
||||
std::string tinypose_128x96::base64Decode(const char *Data, int DataByte) {
|
||||
const char DecodeTable[] = {
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
62, // '+'
|
||||
0, 0, 0,
|
||||
63, // '/'
|
||||
52, 53, 54, 55, 56, 57, 58, 59, 60, 61, // '0'-'9'
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
|
||||
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, // 'A'-'Z'
|
||||
0, 0, 0, 0, 0, 0, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
|
||||
37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, // 'a'-'z'
|
||||
};
|
||||
|
||||
std::string strDecode;
|
||||
int nValue;
|
||||
int i = 0;
|
||||
while (i < DataByte) {
|
||||
if (*Data != '\r' && *Data != '\n') {
|
||||
nValue = DecodeTable[*Data++] << 18;
|
||||
nValue += DecodeTable[*Data++] << 12;
|
||||
strDecode += (nValue & 0x00FF0000) >> 16;
|
||||
if (*Data != '=') {
|
||||
nValue += DecodeTable[*Data++] << 6;
|
||||
strDecode += (nValue & 0x0000FF00) >> 8;
|
||||
if (*Data != '=') {
|
||||
nValue += DecodeTable[*Data++];
|
||||
strDecode += nValue & 0x000000FF;
|
||||
}
|
||||
}
|
||||
i += 4;
|
||||
} else // 回车换行,跳过
|
||||
{
|
||||
Data++;
|
||||
i++;
|
||||
}
|
||||
}
|
||||
return strDecode;
|
||||
}
|
||||
|
||||
DEFINE_OP(tinypose_128x96);
|
||||
|
||||
} // namespace serving
|
||||
} // namespace paddle_serving
|
||||
} // namespace baidu
|
||||
@@ -0,0 +1,69 @@
|
||||
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#pragma once
|
||||
#include "core/general-server/general_model_service.pb.h"
|
||||
#include "core/general-server/op/general_infer_helper.h"
|
||||
#include "paddle_inference_api.h" // NOLINT
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
#include "opencv2/core.hpp"
|
||||
#include "opencv2/imgcodecs.hpp"
|
||||
#include "opencv2/imgproc.hpp"
|
||||
#include <chrono>
|
||||
#include <iomanip>
|
||||
#include <iostream>
|
||||
#include <ostream>
|
||||
#include <vector>
|
||||
|
||||
#include <cstring>
|
||||
#include <fstream>
|
||||
#include <numeric>
|
||||
|
||||
namespace baidu {
|
||||
namespace paddle_serving {
|
||||
namespace serving {
|
||||
|
||||
class tinypose_128x96
|
||||
: public baidu::paddle_serving::predictor::OpWithChannel<GeneralBlob> {
|
||||
public:
|
||||
typedef std::vector<paddle::PaddleTensor> TensorVector;
|
||||
|
||||
DECLARE_OP(tinypose_128x96);
|
||||
|
||||
int inference();
|
||||
|
||||
private:
|
||||
// preprocess
|
||||
std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
|
||||
std::vector<float> scale_ = {0.229f, 0.224f, 0.225f};
|
||||
bool is_scale_ = true;
|
||||
int im_shape_h = 128;
|
||||
int im_shape_w = 96;
|
||||
float scale_factor_h = 1.0f;
|
||||
float scale_factor_w = 1.0f;
|
||||
void preprocess_det(const cv::Mat &img, float *data, float &scale_factor_h,
|
||||
float &scale_factor_w, int im_shape_h, int im_shape_w,
|
||||
const std::vector<float> &mean,
|
||||
const std::vector<float> &scale, const bool is_scale);
|
||||
|
||||
// read pics
|
||||
cv::Mat Base2Mat(std::string &base64_data);
|
||||
std::string base64Decode(const char *Data, int DataByte);
|
||||
};
|
||||
|
||||
} // namespace serving
|
||||
} // namespace paddle_serving
|
||||
} // namespace baidu
|
||||
@@ -0,0 +1,282 @@
|
||||
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#include "core/general-server/op/yolov3_darknet53_270e_coco.h"
|
||||
#include "core/predictor/framework/infer.h"
|
||||
#include "core/predictor/framework/memory.h"
|
||||
#include "core/predictor/framework/resource.h"
|
||||
#include "core/util/include/timer.h"
|
||||
#include <algorithm>
|
||||
#include <iostream>
|
||||
#include <memory>
|
||||
#include <sstream>
|
||||
|
||||
namespace baidu {
|
||||
namespace paddle_serving {
|
||||
namespace serving {
|
||||
|
||||
using baidu::paddle_serving::Timer;
|
||||
using baidu::paddle_serving::predictor::InferManager;
|
||||
using baidu::paddle_serving::predictor::MempoolWrapper;
|
||||
using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
|
||||
using baidu::paddle_serving::predictor::general_model::Request;
|
||||
using baidu::paddle_serving::predictor::general_model::Response;
|
||||
using baidu::paddle_serving::predictor::general_model::Tensor;
|
||||
|
||||
int yolov3_darknet53_270e_coco::inference() {
|
||||
VLOG(2) << "Going to run inference";
|
||||
const std::vector<std::string> pre_node_names = pre_names();
|
||||
if (pre_node_names.size() != 1) {
|
||||
LOG(ERROR) << "This op(" << op_name()
|
||||
<< ") can only have one predecessor op, but received "
|
||||
<< pre_node_names.size();
|
||||
return -1;
|
||||
}
|
||||
const std::string pre_name = pre_node_names[0];
|
||||
|
||||
const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name);
|
||||
if (!input_blob) {
|
||||
LOG(ERROR) << "input_blob is nullptr,error";
|
||||
return -1;
|
||||
}
|
||||
uint64_t log_id = input_blob->GetLogId();
|
||||
VLOG(2) << "(logid=" << log_id << ") Get precedent op name: " << pre_name;
|
||||
|
||||
GeneralBlob *output_blob = mutable_data<GeneralBlob>();
|
||||
if (!output_blob) {
|
||||
LOG(ERROR) << "output_blob is nullptr,error";
|
||||
return -1;
|
||||
}
|
||||
output_blob->SetLogId(log_id);
|
||||
|
||||
if (!input_blob) {
|
||||
LOG(ERROR) << "(logid=" << log_id
|
||||
<< ") Failed mutable depended argument, op:" << pre_name;
|
||||
return -1;
|
||||
}
|
||||
|
||||
const TensorVector *in = &input_blob->tensor_vector;
|
||||
TensorVector *out = &output_blob->tensor_vector;
|
||||
|
||||
int batch_size = input_blob->_batch_size;
|
||||
output_blob->_batch_size = batch_size;
|
||||
VLOG(2) << "(logid=" << log_id << ") infer batch size: " << batch_size;
|
||||
|
||||
Timer timeline;
|
||||
int64_t start = timeline.TimeStampUS();
|
||||
timeline.Start();
|
||||
|
||||
// only support string type
|
||||
char *total_input_ptr = static_cast<char *>(in->at(0).data.data());
|
||||
std::string base64str = total_input_ptr;
|
||||
|
||||
cv::Mat img = Base2Mat(base64str);
|
||||
cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
|
||||
|
||||
// preprocess
|
||||
std::vector<float> input(1 * 3 * im_shape_h * im_shape_w, 0.0f);
|
||||
preprocess_det(img, input.data(), scale_factor_h, scale_factor_w, im_shape_h,
|
||||
im_shape_w, mean_, scale_, is_scale_);
|
||||
|
||||
// create real_in
|
||||
TensorVector *real_in = new TensorVector();
|
||||
if (!real_in) {
|
||||
LOG(ERROR) << "real_in is nullptr,error";
|
||||
return -1;
|
||||
}
|
||||
|
||||
int in_num = 0;
|
||||
size_t databuf_size = 0;
|
||||
void *databuf_data = NULL;
|
||||
char *databuf_char = NULL;
|
||||
|
||||
// im_shape
|
||||
std::vector<float> im_shape{static_cast<float>(im_shape_h),
|
||||
static_cast<float>(im_shape_w)};
|
||||
databuf_size = 2 * sizeof(float);
|
||||
|
||||
databuf_data = MempoolWrapper::instance().malloc(databuf_size);
|
||||
if (!databuf_data) {
|
||||
LOG(ERROR) << "Malloc failed, size: " << databuf_size;
|
||||
return -1;
|
||||
}
|
||||
|
||||
memcpy(databuf_data, im_shape.data(), databuf_size);
|
||||
databuf_char = reinterpret_cast<char *>(databuf_data);
|
||||
paddle::PaddleBuf paddleBuf_0(databuf_char, databuf_size);
|
||||
paddle::PaddleTensor tensor_in_0;
|
||||
tensor_in_0.name = "im_shape";
|
||||
tensor_in_0.dtype = paddle::PaddleDType::FLOAT32;
|
||||
tensor_in_0.shape = {1, 2};
|
||||
tensor_in_0.lod = in->at(0).lod;
|
||||
tensor_in_0.data = paddleBuf_0;
|
||||
real_in->push_back(tensor_in_0);
|
||||
|
||||
// image
|
||||
in_num = 1 * 3 * im_shape_h * im_shape_w;
|
||||
databuf_size = in_num * sizeof(float);
|
||||
|
||||
databuf_data = MempoolWrapper::instance().malloc(databuf_size);
|
||||
if (!databuf_data) {
|
||||
LOG(ERROR) << "Malloc failed, size: " << databuf_size;
|
||||
return -1;
|
||||
}
|
||||
|
||||
memcpy(databuf_data, input.data(), databuf_size);
|
||||
databuf_char = reinterpret_cast<char *>(databuf_data);
|
||||
paddle::PaddleBuf paddleBuf_1(databuf_char, databuf_size);
|
||||
paddle::PaddleTensor tensor_in_1;
|
||||
tensor_in_1.name = "image";
|
||||
tensor_in_1.dtype = paddle::PaddleDType::FLOAT32;
|
||||
tensor_in_1.shape = {1, 3, im_shape_h, im_shape_w};
|
||||
tensor_in_1.lod = in->at(0).lod;
|
||||
tensor_in_1.data = paddleBuf_1;
|
||||
real_in->push_back(tensor_in_1);
|
||||
|
||||
// scale_factor
|
||||
std::vector<float> scale_factor{scale_factor_h, scale_factor_w};
|
||||
databuf_size = 2 * sizeof(float);
|
||||
|
||||
databuf_data = MempoolWrapper::instance().malloc(databuf_size);
|
||||
if (!databuf_data) {
|
||||
LOG(ERROR) << "Malloc failed, size: " << databuf_size;
|
||||
return -1;
|
||||
}
|
||||
|
||||
memcpy(databuf_data, scale_factor.data(), databuf_size);
|
||||
databuf_char = reinterpret_cast<char *>(databuf_data);
|
||||
paddle::PaddleBuf paddleBuf_2(databuf_char, databuf_size);
|
||||
paddle::PaddleTensor tensor_in_2;
|
||||
tensor_in_2.name = "scale_factor";
|
||||
tensor_in_2.dtype = paddle::PaddleDType::FLOAT32;
|
||||
tensor_in_2.shape = {1, 2};
|
||||
tensor_in_2.lod = in->at(0).lod;
|
||||
tensor_in_2.data = paddleBuf_2;
|
||||
real_in->push_back(tensor_in_2);
|
||||
|
||||
if (InferManager::instance().infer(engine_name().c_str(), real_in, out,
|
||||
batch_size)) {
|
||||
LOG(ERROR) << "(logid=" << log_id
|
||||
<< ") Failed do infer in fluid model: " << engine_name().c_str();
|
||||
return -1;
|
||||
}
|
||||
|
||||
int64_t end = timeline.TimeStampUS();
|
||||
CopyBlobInfo(input_blob, output_blob);
|
||||
AddBlobInfo(output_blob, start);
|
||||
AddBlobInfo(output_blob, end);
|
||||
return 0;
|
||||
}
|
||||
|
||||
void yolov3_darknet53_270e_coco::preprocess_det(const cv::Mat &img, float *data,
|
||||
float &scale_factor_h,
|
||||
float &scale_factor_w,
|
||||
int im_shape_h, int im_shape_w,
|
||||
const std::vector<float> &mean,
|
||||
const std::vector<float> &scale,
|
||||
const bool is_scale) {
|
||||
// scale_factor
|
||||
scale_factor_h =
|
||||
static_cast<float>(im_shape_h) / static_cast<float>(img.rows);
|
||||
scale_factor_w =
|
||||
static_cast<float>(im_shape_w) / static_cast<float>(img.cols);
|
||||
|
||||
// Resize
|
||||
cv::Mat resize_img;
|
||||
cv::resize(img, resize_img, cv::Size(im_shape_w, im_shape_h), 0, 0, 2);
|
||||
|
||||
// Normalize
|
||||
double e = 1.0;
|
||||
if (is_scale) {
|
||||
e /= 255.0;
|
||||
}
|
||||
cv::Mat img_fp;
|
||||
(resize_img).convertTo(img_fp, CV_32FC3, e);
|
||||
for (int h = 0; h < im_shape_h; h++) {
|
||||
for (int w = 0; w < im_shape_w; w++) {
|
||||
img_fp.at<cv::Vec3f>(h, w)[0] =
|
||||
(img_fp.at<cv::Vec3f>(h, w)[0] - mean[0]) / scale[0];
|
||||
img_fp.at<cv::Vec3f>(h, w)[1] =
|
||||
(img_fp.at<cv::Vec3f>(h, w)[1] - mean[1]) / scale[1];
|
||||
img_fp.at<cv::Vec3f>(h, w)[2] =
|
||||
(img_fp.at<cv::Vec3f>(h, w)[2] - mean[2]) / scale[2];
|
||||
}
|
||||
}
|
||||
|
||||
// Permute
|
||||
int rh = img_fp.rows;
|
||||
int rw = img_fp.cols;
|
||||
int rc = img_fp.channels();
|
||||
for (int i = 0; i < rc; ++i) {
|
||||
cv::extractChannel(img_fp, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw),
|
||||
i);
|
||||
}
|
||||
}
|
||||
|
||||
cv::Mat yolov3_darknet53_270e_coco::Base2Mat(std::string &base64_data) {
|
||||
cv::Mat img;
|
||||
std::string s_mat;
|
||||
s_mat = base64Decode(base64_data.data(), base64_data.size());
|
||||
std::vector<char> base64_img(s_mat.begin(), s_mat.end());
|
||||
img = cv::imdecode(base64_img, cv::IMREAD_COLOR); // CV_LOAD_IMAGE_COLOR
|
||||
return img;
|
||||
}
|
||||
|
||||
std::string yolov3_darknet53_270e_coco::base64Decode(const char *Data,
|
||||
int DataByte) {
|
||||
const char DecodeTable[] = {
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
62, // '+'
|
||||
0, 0, 0,
|
||||
63, // '/'
|
||||
52, 53, 54, 55, 56, 57, 58, 59, 60, 61, // '0'-'9'
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
|
||||
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, // 'A'-'Z'
|
||||
0, 0, 0, 0, 0, 0, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
|
||||
37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, // 'a'-'z'
|
||||
};
|
||||
|
||||
std::string strDecode;
|
||||
int nValue;
|
||||
int i = 0;
|
||||
while (i < DataByte) {
|
||||
if (*Data != '\r' && *Data != '\n') {
|
||||
nValue = DecodeTable[*Data++] << 18;
|
||||
nValue += DecodeTable[*Data++] << 12;
|
||||
strDecode += (nValue & 0x00FF0000) >> 16;
|
||||
if (*Data != '=') {
|
||||
nValue += DecodeTable[*Data++] << 6;
|
||||
strDecode += (nValue & 0x0000FF00) >> 8;
|
||||
if (*Data != '=') {
|
||||
nValue += DecodeTable[*Data++];
|
||||
strDecode += nValue & 0x000000FF;
|
||||
}
|
||||
}
|
||||
i += 4;
|
||||
} else // 回车换行,跳过
|
||||
{
|
||||
Data++;
|
||||
i++;
|
||||
}
|
||||
}
|
||||
return strDecode;
|
||||
}
|
||||
|
||||
DEFINE_OP(yolov3_darknet53_270e_coco);
|
||||
|
||||
} // namespace serving
|
||||
} // namespace paddle_serving
|
||||
} // namespace baidu
|
||||
@@ -0,0 +1,69 @@
|
||||
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#pragma once
|
||||
#include "core/general-server/general_model_service.pb.h"
|
||||
#include "core/general-server/op/general_infer_helper.h"
|
||||
#include "paddle_inference_api.h" // NOLINT
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
#include "opencv2/core.hpp"
|
||||
#include "opencv2/imgcodecs.hpp"
|
||||
#include "opencv2/imgproc.hpp"
|
||||
#include <chrono>
|
||||
#include <iomanip>
|
||||
#include <iostream>
|
||||
#include <ostream>
|
||||
#include <vector>
|
||||
|
||||
#include <cstring>
|
||||
#include <fstream>
|
||||
#include <numeric>
|
||||
|
||||
namespace baidu {
|
||||
namespace paddle_serving {
|
||||
namespace serving {
|
||||
|
||||
class yolov3_darknet53_270e_coco
|
||||
: public baidu::paddle_serving::predictor::OpWithChannel<GeneralBlob> {
|
||||
public:
|
||||
typedef std::vector<paddle::PaddleTensor> TensorVector;
|
||||
|
||||
DECLARE_OP(yolov3_darknet53_270e_coco);
|
||||
|
||||
int inference();
|
||||
|
||||
private:
|
||||
// preprocess
|
||||
std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
|
||||
std::vector<float> scale_ = {0.229f, 0.224f, 0.225f};
|
||||
bool is_scale_ = true;
|
||||
int im_shape_h = 608;
|
||||
int im_shape_w = 608;
|
||||
float scale_factor_h = 1.0f;
|
||||
float scale_factor_w = 1.0f;
|
||||
void preprocess_det(const cv::Mat &img, float *data, float &scale_factor_h,
|
||||
float &scale_factor_w, int im_shape_h, int im_shape_w,
|
||||
const std::vector<float> &mean,
|
||||
const std::vector<float> &scale, const bool is_scale);
|
||||
|
||||
// read pics
|
||||
cv::Mat Base2Mat(std::string &base64_data);
|
||||
std::string base64Decode(const char *Data, int DataByte);
|
||||
};
|
||||
|
||||
} // namespace serving
|
||||
} // namespace paddle_serving
|
||||
} // namespace baidu
|
||||
125
paddle_detection/deploy/serving/cpp/serving_client.py
Normal file
125
paddle_detection/deploy/serving/cpp/serving_client.py
Normal file
@@ -0,0 +1,125 @@
|
||||
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import os
|
||||
import glob
|
||||
import base64
|
||||
import argparse
|
||||
from paddle_serving_client import Client
|
||||
from paddle_serving_client.proto import general_model_config_pb2 as m_config
|
||||
import google.protobuf.text_format
|
||||
|
||||
parser = argparse.ArgumentParser(description="args for paddleserving")
|
||||
parser.add_argument(
|
||||
"--serving_client", type=str, help="the directory of serving_client")
|
||||
parser.add_argument("--image_dir", type=str)
|
||||
parser.add_argument("--image_file", type=str)
|
||||
parser.add_argument("--http_port", type=int, default=9997)
|
||||
parser.add_argument(
|
||||
"--threshold", type=float, default=0.5, help="Threshold of score.")
|
||||
args = parser.parse_args()
|
||||
|
||||
|
||||
def get_test_images(infer_dir, infer_img):
|
||||
"""
|
||||
Get image path list in TEST mode
|
||||
"""
|
||||
assert infer_img is not None or infer_dir is not None, \
|
||||
"--image_file or --image_dir should be set"
|
||||
assert infer_img is None or os.path.isfile(infer_img), \
|
||||
"{} is not a file".format(infer_img)
|
||||
assert infer_dir is None or os.path.isdir(infer_dir), \
|
||||
"{} is not a directory".format(infer_dir)
|
||||
|
||||
# infer_img has a higher priority
|
||||
if infer_img and os.path.isfile(infer_img):
|
||||
return [infer_img]
|
||||
|
||||
images = set()
|
||||
infer_dir = os.path.abspath(infer_dir)
|
||||
assert os.path.isdir(infer_dir), \
|
||||
"infer_dir {} is not a directory".format(infer_dir)
|
||||
exts = ['jpg', 'jpeg', 'png', 'bmp']
|
||||
exts += [ext.upper() for ext in exts]
|
||||
for ext in exts:
|
||||
images.update(glob.glob('{}/*.{}'.format(infer_dir, ext)))
|
||||
images = list(images)
|
||||
|
||||
assert len(images) > 0, "no image found in {}".format(infer_dir)
|
||||
print("Found {} inference images in total.".format(len(images)))
|
||||
|
||||
return images
|
||||
|
||||
|
||||
def postprocess(fetch_dict, fetch_vars, draw_threshold=0.5):
|
||||
result = []
|
||||
if "conv2d_441.tmp_1" in fetch_dict:
|
||||
heatmap = fetch_dict["conv2d_441.tmp_1"]
|
||||
print(heatmap)
|
||||
result.append(heatmap)
|
||||
else:
|
||||
bboxes = fetch_dict[fetch_vars[0]]
|
||||
for bbox in bboxes:
|
||||
if bbox[0] > -1 and bbox[1] > draw_threshold:
|
||||
print(f"{int(bbox[0])} {bbox[1]} "
|
||||
f"{bbox[2]} {bbox[3]} {bbox[4]} {bbox[5]}")
|
||||
result.append(f"{int(bbox[0])} {bbox[1]} "
|
||||
f"{bbox[2]} {bbox[3]} {bbox[4]} {bbox[5]}")
|
||||
return result
|
||||
|
||||
|
||||
def get_model_vars(client_config_dir):
|
||||
# read original serving_client_conf.prototxt
|
||||
client_config_file = os.path.join(client_config_dir,
|
||||
"serving_client_conf.prototxt")
|
||||
with open(client_config_file, 'r') as f:
|
||||
model_var = google.protobuf.text_format.Merge(
|
||||
str(f.read()), m_config.GeneralModelConfig())
|
||||
# modify feed_var to run core/general-server/op/
|
||||
[model_var.feed_var.pop() for _ in range(len(model_var.feed_var))]
|
||||
feed_var = m_config.FeedVar()
|
||||
feed_var.name = "input"
|
||||
feed_var.alias_name = "input"
|
||||
feed_var.is_lod_tensor = False
|
||||
feed_var.feed_type = 20
|
||||
feed_var.shape.extend([1])
|
||||
model_var.feed_var.extend([feed_var])
|
||||
with open(
|
||||
os.path.join(client_config_dir, "serving_client_conf_cpp.prototxt"),
|
||||
"w") as f:
|
||||
f.write(str(model_var))
|
||||
# get feed_vars/fetch_vars
|
||||
feed_vars = [var.name for var in model_var.feed_var]
|
||||
fetch_vars = [var.name for var in model_var.fetch_var]
|
||||
return feed_vars, fetch_vars
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
url = f"127.0.0.1:{args.http_port}"
|
||||
logid = 10000
|
||||
img_list = get_test_images(args.image_dir, args.image_file)
|
||||
feed_vars, fetch_vars = get_model_vars(args.serving_client)
|
||||
|
||||
client = Client()
|
||||
client.load_client_config(
|
||||
os.path.join(args.serving_client, "serving_client_conf_cpp.prototxt"))
|
||||
client.connect([url])
|
||||
|
||||
for img_file in img_list:
|
||||
with open(img_file, 'rb') as file:
|
||||
image_data = file.read()
|
||||
image = base64.b64encode(image_data).decode('utf8')
|
||||
fetch_dict = client.predict(
|
||||
feed={feed_vars[0]: image}, fetch=fetch_vars)
|
||||
result = postprocess(fetch_dict, fetch_vars, args.threshold)
|
||||
@@ -0,0 +1,20 @@
|
||||
feed_var {
|
||||
name: "input"
|
||||
alias_name: "input"
|
||||
is_lod_tensor: false
|
||||
feed_type: 20
|
||||
shape: 1
|
||||
}
|
||||
fetch_var {
|
||||
name: "multiclass_nms3_0.tmp_0"
|
||||
alias_name: "multiclass_nms3_0.tmp_0"
|
||||
is_lod_tensor: true
|
||||
fetch_type: 1
|
||||
shape: -1
|
||||
}
|
||||
fetch_var {
|
||||
name: "multiclass_nms3_0.tmp_2"
|
||||
alias_name: "multiclass_nms3_0.tmp_2"
|
||||
is_lod_tensor: false
|
||||
fetch_type: 2
|
||||
}
|
||||
80
paddle_detection/deploy/serving/label_list.txt
Normal file
80
paddle_detection/deploy/serving/label_list.txt
Normal file
@@ -0,0 +1,80 @@
|
||||
person
|
||||
bicycle
|
||||
car
|
||||
motorcycle
|
||||
airplane
|
||||
bus
|
||||
train
|
||||
truck
|
||||
boat
|
||||
traffic light
|
||||
fire hydrant
|
||||
stop sign
|
||||
parking meter
|
||||
bench
|
||||
bird
|
||||
cat
|
||||
dog
|
||||
horse
|
||||
sheep
|
||||
cow
|
||||
elephant
|
||||
bear
|
||||
zebra
|
||||
giraffe
|
||||
backpack
|
||||
umbrella
|
||||
handbag
|
||||
tie
|
||||
suitcase
|
||||
frisbee
|
||||
skis
|
||||
snowboard
|
||||
sports ball
|
||||
kite
|
||||
baseball bat
|
||||
baseball glove
|
||||
skateboard
|
||||
surfboard
|
||||
tennis racket
|
||||
bottle
|
||||
wine glass
|
||||
cup
|
||||
fork
|
||||
knife
|
||||
spoon
|
||||
bowl
|
||||
banana
|
||||
apple
|
||||
sandwich
|
||||
orange
|
||||
broccoli
|
||||
carrot
|
||||
hot dog
|
||||
pizza
|
||||
donut
|
||||
cake
|
||||
chair
|
||||
couch
|
||||
potted plant
|
||||
bed
|
||||
dining table
|
||||
toilet
|
||||
tv
|
||||
laptop
|
||||
mouse
|
||||
remote
|
||||
keyboard
|
||||
cell phone
|
||||
microwave
|
||||
oven
|
||||
toaster
|
||||
sink
|
||||
refrigerator
|
||||
book
|
||||
clock
|
||||
vase
|
||||
scissors
|
||||
teddy bear
|
||||
hair drier
|
||||
toothbrush
|
||||
72
paddle_detection/deploy/serving/python/README.md
Normal file
72
paddle_detection/deploy/serving/python/README.md
Normal file
@@ -0,0 +1,72 @@
|
||||
# Python Serving预测部署
|
||||
|
||||
## 1. 简介
|
||||
Paddle Serving是飞桨开源的服务化部署框架，提供了C++ Serving和Python Pipeline两套框架，
|
||||
C++ Serving框架更倾向于追求极致性能，Python Pipeline框架倾向于二次开发的便捷性。
|
||||
旨在帮助深度学习开发者和企业提供高性能、灵活易用的工业级在线推理服务，助力人工智能落地应用。
|
||||
|
||||
更多关于Paddle Serving的介绍，可以参考[Paddle Serving官网repo](https://github.com/PaddlePaddle/Serving)。
|
||||
|
||||
本文档主要介绍利用Python Pipeline框架实现模型（以yolov3_darknet53_270e_coco为例）的服务化部署。
|
||||
|
||||
## 2. Python Serving预测部署
|
||||
|
||||
#### 2.1 Python 服务化部署样例程序介绍
|
||||
服务化部署的样例程序的目录地址为：`deploy/serving/python`
|
||||
```shell
|
||||
deploy/
|
||||
├── serving/
|
||||
│ ├── python/ # Python 服务化部署样例程序目录
|
||||
│ │ ├──config.yml # 服务端模型预测相关配置文件
|
||||
│ │ ├──pipeline_http_client.py # 客户端代码
|
||||
│ │ ├──postprocess_ops.py # 用户自定义后处理代码
|
||||
│ │ ├──preprocess_ops.py # 用户自定义预处理代码
|
||||
│ │ ├──README.md # 说明文档
|
||||
│ │ ├──web_service.py # 服务端代码
|
||||
│ ├── cpp/ # C++ 服务化部署样例程序目录
|
||||
│ │ ├──preprocess/ # C++ 自定义OP
|
||||
│ │ ├──build_server.sh # C++ Serving 编译脚本
|
||||
│ │ ├──serving_client.py # 客户端代码
|
||||
│ │ └── ...
|
||||
│ └── ...
|
||||
└── ...
|
||||
```
|
||||
|
||||
### 2.2 环境准备
|
||||
安装Paddle Serving四个安装包的最新版本，
|
||||
分别是：paddle-serving-server(CPU/GPU版本二选一),
|
||||
paddle-serving-client, paddle-serving-app和paddlepaddle(CPU/GPU版本二选一)。
|
||||
```commandline
|
||||
pip install paddle-serving-client
|
||||
# pip install paddle-serving-server # CPU
|
||||
pip install paddle-serving-server-gpu # GPU 默认 CUDA10.2 + TensorRT6，其他环境需手动指定版本号
|
||||
pip install paddle-serving-app
|
||||
# pip install paddlepaddle # CPU
|
||||
pip install paddlepaddle-gpu
|
||||
```
|
||||
您可能需要使用国内镜像源（例如百度源, 在pip命令中添加`-i https://mirror.baidu.com/pypi/simple`）来加速下载。
|
||||
Paddle Serving Server更多不同运行环境的whl包下载地址，请参考：[下载页面](https://github.com/PaddlePaddle/Serving/blob/v0.7.0/doc/Latest_Packages_CN.md)
|
||||
PaddlePaddle更多版本请参考[官网](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html)
|
||||
|
||||
### 2.3 服务化部署模型导出
|
||||
导出步骤参考文档[PaddleDetection部署模型导出教程](../../EXPORT_MODEL.md),
|
||||
导出服务化部署模型需要添加`--export_serving_model True`参数，导出示例如下:
|
||||
```commandline
|
||||
python tools/export_model.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml \
|
||||
--export_serving_model True \
|
||||
-o weights=https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams
|
||||
```
|
||||
|
||||
### 2.4 启动服务端模型预测服务
|
||||
当完成以上环境准备和模型导出后，可以按如下命令启动模型预测服务：
|
||||
```commandline
|
||||
python deploy/serving/python/web_service.py --model_dir output_inference/yolov3_darknet53_270e_coco &
|
||||
```
|
||||
服务端模型预测相关配置可在[config.yml](./config.yml)中修改，
|
||||
开发者只需要关注如下配置：http_port（服务的http端口），device_type（计算硬件类型），devices（计算硬件ID）。
|
||||
|
||||
### 2.5 启动客户端访问
|
||||
当成功启动了模型预测服务，可以按如下命令启动客户端访问服务：
|
||||
```commandline
|
||||
python deploy/serving/python/pipeline_http_client.py --image_file demo/000000014439.jpg
|
||||
```
|
||||
31
paddle_detection/deploy/serving/python/config.yml
Normal file
31
paddle_detection/deploy/serving/python/config.yml
Normal file
@@ -0,0 +1,31 @@
|
||||
#worker_num, 最大并发数。当build_dag_each_worker=True时, 框架会创建worker_num个进程，每个进程内构建grpcSever和DAG
|
||||
##当build_dag_each_worker=False时，框架会设置主线程grpc线程池的max_workers=worker_num
|
||||
worker_num: 20
|
||||
|
||||
#http端口, rpc_port和http_port不允许同时为空。当rpc_port可用且http_port为空时，不自动生成http_port
|
||||
http_port: 18093
|
||||
rpc_port: 9993
|
||||
|
||||
dag:
|
||||
#op资源类型, True, 为线程模型；False，为进程模型
|
||||
is_thread_op: False
|
||||
op:
|
||||
#op名称，与web_service中的TIPCExampleService初始化name参数一致
|
||||
ppdet:
|
||||
#并发数，is_thread_op=True时，为线程并发；否则为进程并发
|
||||
concurrency: 1
|
||||
|
||||
#当op配置没有server_endpoints时，从local_service_conf读取本地服务配置
|
||||
local_service_conf:
|
||||
|
||||
#uci模型路径
|
||||
model_config: "./serving_server"
|
||||
|
||||
#计算硬件类型: 空缺时由devices决定(CPU/GPU)，0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu
|
||||
device_type:
|
||||
|
||||
#计算硬件ID，当devices为""或不写时为CPU预测；当devices为"0", "0,1,2"时为GPU预测，表示使用的GPU卡
|
||||
devices: "0" # "0,1"
|
||||
|
||||
#client类型，包括brpc, grpc和local_predictor.local_predictor不启动Serving服务，进程内预测
|
||||
client_type: local_predictor
|
||||
@@ -0,0 +1,76 @@
|
||||
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import glob
|
||||
import requests
|
||||
import json
|
||||
import base64
|
||||
import os
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser(description="args for paddleserving")
|
||||
parser.add_argument("--image_dir", type=str)
|
||||
parser.add_argument("--image_file", type=str)
|
||||
parser.add_argument("--http_port", type=int, default=18093)
|
||||
parser.add_argument("--service_name", type=str, default="ppdet")
|
||||
args = parser.parse_args()
|
||||
|
||||
|
||||
def get_test_images(infer_dir, infer_img):
|
||||
"""
|
||||
Get image path list in TEST mode
|
||||
"""
|
||||
assert infer_img is not None or infer_dir is not None, \
|
||||
"--image_file or --image_dir should be set"
|
||||
assert infer_img is None or os.path.isfile(infer_img), \
|
||||
"{} is not a file".format(infer_img)
|
||||
assert infer_dir is None or os.path.isdir(infer_dir), \
|
||||
"{} is not a directory".format(infer_dir)
|
||||
|
||||
# infer_img has a higher priority
|
||||
if infer_img and os.path.isfile(infer_img):
|
||||
return [infer_img]
|
||||
|
||||
images = set()
|
||||
infer_dir = os.path.abspath(infer_dir)
|
||||
assert os.path.isdir(infer_dir), \
|
||||
"infer_dir {} is not a directory".format(infer_dir)
|
||||
exts = ['jpg', 'jpeg', 'png', 'bmp']
|
||||
exts += [ext.upper() for ext in exts]
|
||||
for ext in exts:
|
||||
images.update(glob.glob('{}/*.{}'.format(infer_dir, ext)))
|
||||
images = list(images)
|
||||
|
||||
assert len(images) > 0, "no image found in {}".format(infer_dir)
|
||||
print("Found {} inference images in total.".format(len(images)))
|
||||
|
||||
return images
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
url = f"http://127.0.0.1:{args.http_port}/{args.service_name}/prediction"
|
||||
logid = 10000
|
||||
img_list = get_test_images(args.image_dir, args.image_file)
|
||||
|
||||
for img_file in img_list:
|
||||
with open(img_file, 'rb') as file:
|
||||
image_data = file.read()
|
||||
|
||||
# base64 encode
|
||||
image = base64.b64encode(image_data).decode('utf8')
|
||||
|
||||
data = {"key": ["image_0"], "value": [image], "logid": logid}
|
||||
# send requests
|
||||
r = requests.post(url=url, data=json.dumps(data))
|
||||
print(r.json())
|
||||
171
paddle_detection/deploy/serving/python/postprocess_ops.py
Normal file
171
paddle_detection/deploy/serving/python/postprocess_ops.py
Normal file
@@ -0,0 +1,171 @@
|
||||
import cv2
|
||||
import math
|
||||
import numpy as np
|
||||
from preprocess_ops import get_affine_transform
|
||||
|
||||
|
||||
class HRNetPostProcess(object):
|
||||
def __init__(self, use_dark=True):
|
||||
self.use_dark = use_dark
|
||||
|
||||
def flip_back(self, output_flipped, matched_parts):
|
||||
assert output_flipped.ndim == 4,\
|
||||
'output_flipped should be [batch_size, num_joints, height, width]'
|
||||
|
||||
output_flipped = output_flipped[:, :, :, ::-1]
|
||||
|
||||
for pair in matched_parts:
|
||||
tmp = output_flipped[:, pair[0], :, :].copy()
|
||||
output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :]
|
||||
output_flipped[:, pair[1], :, :] = tmp
|
||||
|
||||
return output_flipped
|
||||
|
||||
def get_max_preds(self, heatmaps):
|
||||
"""get predictions from score maps
|
||||
|
||||
Args:
|
||||
heatmaps: numpy.ndarray([batch_size, num_joints, height, width])
|
||||
|
||||
Returns:
|
||||
preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords
|
||||
maxvals: numpy.ndarray([batch_size, num_joints, 2]), the maximum confidence of the keypoints
|
||||
"""
|
||||
assert isinstance(heatmaps,
|
||||
np.ndarray), 'heatmaps should be numpy.ndarray'
|
||||
assert heatmaps.ndim == 4, 'batch_images should be 4-ndim'
|
||||
|
||||
batch_size = heatmaps.shape[0]
|
||||
num_joints = heatmaps.shape[1]
|
||||
width = heatmaps.shape[3]
|
||||
heatmaps_reshaped = heatmaps.reshape((batch_size, num_joints, -1))
|
||||
idx = np.argmax(heatmaps_reshaped, 2)
|
||||
maxvals = np.amax(heatmaps_reshaped, 2)
|
||||
|
||||
maxvals = maxvals.reshape((batch_size, num_joints, 1))
|
||||
idx = idx.reshape((batch_size, num_joints, 1))
|
||||
|
||||
preds = np.tile(idx, (1, 1, 2)).astype(np.float32)
|
||||
|
||||
preds[:, :, 0] = (preds[:, :, 0]) % width
|
||||
preds[:, :, 1] = np.floor((preds[:, :, 1]) / width)
|
||||
|
||||
pred_mask = np.tile(np.greater(maxvals, 0.0), (1, 1, 2))
|
||||
pred_mask = pred_mask.astype(np.float32)
|
||||
|
||||
preds *= pred_mask
|
||||
|
||||
return preds, maxvals
|
||||
|
||||
def gaussian_blur(self, heatmap, kernel):
|
||||
border = (kernel - 1) // 2
|
||||
batch_size = heatmap.shape[0]
|
||||
num_joints = heatmap.shape[1]
|
||||
height = heatmap.shape[2]
|
||||
width = heatmap.shape[3]
|
||||
for i in range(batch_size):
|
||||
for j in range(num_joints):
|
||||
origin_max = np.max(heatmap[i, j])
|
||||
dr = np.zeros((height + 2 * border, width + 2 * border))
|
||||
dr[border:-border, border:-border] = heatmap[i, j].copy()
|
||||
dr = cv2.GaussianBlur(dr, (kernel, kernel), 0)
|
||||
heatmap[i, j] = dr[border:-border, border:-border].copy()
|
||||
heatmap[i, j] *= origin_max / np.max(heatmap[i, j])
|
||||
return heatmap
|
||||
|
||||
def dark_parse(self, hm, coord):
|
||||
heatmap_height = hm.shape[0]
|
||||
heatmap_width = hm.shape[1]
|
||||
px = int(coord[0])
|
||||
py = int(coord[1])
|
||||
if 1 < px < heatmap_width - 2 and 1 < py < heatmap_height - 2:
|
||||
dx = 0.5 * (hm[py][px + 1] - hm[py][px - 1])
|
||||
dy = 0.5 * (hm[py + 1][px] - hm[py - 1][px])
|
||||
dxx = 0.25 * (hm[py][px + 2] - 2 * hm[py][px] + hm[py][px - 2])
|
||||
dxy = 0.25 * (hm[py+1][px+1] - hm[py-1][px+1] - hm[py+1][px-1] \
|
||||
+ hm[py-1][px-1])
|
||||
dyy = 0.25 * (
|
||||
hm[py + 2 * 1][px] - 2 * hm[py][px] + hm[py - 2 * 1][px])
|
||||
derivative = np.matrix([[dx], [dy]])
|
||||
hessian = np.matrix([[dxx, dxy], [dxy, dyy]])
|
||||
if dxx * dyy - dxy**2 != 0:
|
||||
hessianinv = hessian.I
|
||||
offset = -hessianinv * derivative
|
||||
offset = np.squeeze(np.array(offset.T), axis=0)
|
||||
coord += offset
|
||||
return coord
|
||||
|
||||
def dark_postprocess(self, hm, coords, kernelsize):
|
||||
"""
|
||||
refer to https://github.com/ilovepose/DarkPose/lib/core/inference.py
|
||||
|
||||
"""
|
||||
hm = self.gaussian_blur(hm, kernelsize)
|
||||
hm = np.maximum(hm, 1e-10)
|
||||
hm = np.log(hm)
|
||||
for n in range(coords.shape[0]):
|
||||
for p in range(coords.shape[1]):
|
||||
coords[n, p] = self.dark_parse(hm[n][p], coords[n][p])
|
||||
return coords
|
||||
|
||||
def get_final_preds(self, heatmaps, center, scale, kernelsize=3):
|
||||
"""the highest heatvalue location with a quarter offset in the
|
||||
direction from the highest response to the second highest response.
|
||||
|
||||
Args:
|
||||
heatmaps (numpy.ndarray): The predicted heatmaps
|
||||
center (numpy.ndarray): The boxes center
|
||||
scale (numpy.ndarray): The scale factor
|
||||
|
||||
Returns:
|
||||
preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords
|
||||
maxvals: numpy.ndarray([batch_size, num_joints, 1]), the maximum confidence of the keypoints
|
||||
"""
|
||||
|
||||
coords, maxvals = self.get_max_preds(heatmaps)
|
||||
|
||||
heatmap_height = heatmaps.shape[2]
|
||||
heatmap_width = heatmaps.shape[3]
|
||||
|
||||
if self.use_dark:
|
||||
coords = self.dark_postprocess(heatmaps, coords, kernelsize)
|
||||
else:
|
||||
for n in range(coords.shape[0]):
|
||||
for p in range(coords.shape[1]):
|
||||
hm = heatmaps[n][p]
|
||||
px = int(math.floor(coords[n][p][0] + 0.5))
|
||||
py = int(math.floor(coords[n][p][1] + 0.5))
|
||||
if 1 < px < heatmap_width - 1 and 1 < py < heatmap_height - 1:
|
||||
diff = np.array([
|
||||
hm[py][px + 1] - hm[py][px - 1],
|
||||
hm[py + 1][px] - hm[py - 1][px]
|
||||
])
|
||||
coords[n][p] += np.sign(diff) * .25
|
||||
preds = coords.copy()
|
||||
|
||||
# Transform back
|
||||
for i in range(coords.shape[0]):
|
||||
preds[i] = transform_preds(coords[i], center[i], scale[i],
|
||||
[heatmap_width, heatmap_height])
|
||||
|
||||
return preds, maxvals
|
||||
|
||||
def __call__(self, output, center, scale):
|
||||
preds, maxvals = self.get_final_preds(output, center, scale)
|
||||
return np.concatenate(
|
||||
(preds, maxvals), axis=-1), np.mean(
|
||||
maxvals, axis=1)
|
||||
|
||||
|
||||
def transform_preds(coords, center, scale, output_size):
|
||||
target_coords = np.zeros(coords.shape)
|
||||
trans = get_affine_transform(center, scale * 200, 0, output_size, inv=1)
|
||||
for p in range(coords.shape[0]):
|
||||
target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans)
|
||||
return target_coords
|
||||
|
||||
|
||||
def affine_transform(pt, t):
|
||||
new_pt = np.array([pt[0], pt[1], 1.]).T
|
||||
new_pt = np.dot(t, new_pt)
|
||||
return new_pt[:2]
|
||||
490
paddle_detection/deploy/serving/python/preprocess_ops.py
Normal file
490
paddle_detection/deploy/serving/python/preprocess_ops.py
Normal file
@@ -0,0 +1,490 @@
|
||||
import numpy as np
|
||||
import cv2
|
||||
import copy
|
||||
|
||||
|
||||
def decode_image(im):
|
||||
im = np.array(im)
|
||||
img_info = {
|
||||
"im_shape": np.array(
|
||||
im.shape[:2], dtype=np.float32),
|
||||
"scale_factor": np.array(
|
||||
[1., 1.], dtype=np.float32)
|
||||
}
|
||||
return im, img_info
|
||||
|
||||
|
||||
class Resize(object):
|
||||
"""resize image by target_size and max_size
|
||||
Args:
|
||||
target_size (int): the target size of image
|
||||
keep_ratio (bool): whether keep_ratio or not, default true
|
||||
interp (int): method of resize
|
||||
"""
|
||||
|
||||
def __init__(self, target_size, keep_ratio=True, interp=cv2.INTER_LINEAR):
|
||||
if isinstance(target_size, int):
|
||||
target_size = [target_size, target_size]
|
||||
self.target_size = target_size
|
||||
self.keep_ratio = keep_ratio
|
||||
self.interp = interp
|
||||
|
||||
def __call__(self, im, im_info):
|
||||
"""
|
||||
Args:
|
||||
im (np.ndarray): image (np.ndarray)
|
||||
im_info (dict): info of image
|
||||
Returns:
|
||||
im (np.ndarray): processed image (np.ndarray)
|
||||
im_info (dict): info of processed image
|
||||
"""
|
||||
assert len(self.target_size) == 2
|
||||
assert self.target_size[0] > 0 and self.target_size[1] > 0
|
||||
im_channel = im.shape[2]
|
||||
im_scale_y, im_scale_x = self.generate_scale(im)
|
||||
im = cv2.resize(
|
||||
im,
|
||||
None,
|
||||
None,
|
||||
fx=im_scale_x,
|
||||
fy=im_scale_y,
|
||||
interpolation=self.interp)
|
||||
im_info['im_shape'] = np.array(im.shape[:2]).astype('float32')
|
||||
im_info['scale_factor'] = np.array(
|
||||
[im_scale_y, im_scale_x]).astype('float32')
|
||||
return im, im_info
|
||||
|
||||
def generate_scale(self, im):
|
||||
"""
|
||||
Args:
|
||||
im (np.ndarray): image (np.ndarray)
|
||||
Returns:
|
||||
im_scale_x: the resize ratio of X
|
||||
im_scale_y: the resize ratio of Y
|
||||
"""
|
||||
origin_shape = im.shape[:2]
|
||||
im_c = im.shape[2]
|
||||
if self.keep_ratio:
|
||||
im_size_min = np.min(origin_shape)
|
||||
im_size_max = np.max(origin_shape)
|
||||
target_size_min = np.min(self.target_size)
|
||||
target_size_max = np.max(self.target_size)
|
||||
im_scale = float(target_size_min) / float(im_size_min)
|
||||
if np.round(im_scale * im_size_max) > target_size_max:
|
||||
im_scale = float(target_size_max) / float(im_size_max)
|
||||
im_scale_x = im_scale
|
||||
im_scale_y = im_scale
|
||||
else:
|
||||
resize_h, resize_w = self.target_size
|
||||
im_scale_y = resize_h / float(origin_shape[0])
|
||||
im_scale_x = resize_w / float(origin_shape[1])
|
||||
return im_scale_y, im_scale_x
|
||||
|
||||
|
||||
class NormalizeImage(object):
|
||||
"""normalize image
|
||||
Args:
|
||||
mean (list): im - mean
|
||||
std (list): im / std
|
||||
is_scale (bool): whether need im / 255
|
||||
norm_type (str): type in ['mean_std', 'none']
|
||||
"""
|
||||
|
||||
def __init__(self, mean, std, is_scale=True, norm_type='mean_std'):
|
||||
self.mean = mean
|
||||
self.std = std
|
||||
self.is_scale = is_scale
|
||||
self.norm_type = norm_type
|
||||
|
||||
def __call__(self, im, im_info):
|
||||
"""
|
||||
Args:
|
||||
im (np.ndarray): image (np.ndarray)
|
||||
im_info (dict): info of image
|
||||
Returns:
|
||||
im (np.ndarray): processed image (np.ndarray)
|
||||
im_info (dict): info of processed image
|
||||
"""
|
||||
im = im.astype(np.float32, copy=False)
|
||||
if self.is_scale:
|
||||
scale = 1.0 / 255.0
|
||||
im *= scale
|
||||
|
||||
if self.norm_type == 'mean_std':
|
||||
mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
|
||||
std = np.array(self.std)[np.newaxis, np.newaxis, :]
|
||||
im -= mean
|
||||
im /= std
|
||||
return im, im_info
|
||||
|
||||
|
||||
class Permute(object):
|
||||
"""permute image
|
||||
Args:
|
||||
to_bgr (bool): whether convert RGB to BGR
|
||||
channel_first (bool): whether convert HWC to CHW
|
||||
"""
|
||||
|
||||
def __init__(self, ):
|
||||
super(Permute, self).__init__()
|
||||
|
||||
def __call__(self, im, im_info):
|
||||
"""
|
||||
Args:
|
||||
im (np.ndarray): image (np.ndarray)
|
||||
im_info (dict): info of image
|
||||
Returns:
|
||||
im (np.ndarray): processed image (np.ndarray)
|
||||
im_info (dict): info of processed image
|
||||
"""
|
||||
im = im.transpose((2, 0, 1)).copy()
|
||||
return im, im_info
|
||||
|
||||
|
||||
class PadStride(object):
|
||||
""" padding image for model with FPN, instead PadBatch(pad_to_stride) in original config
|
||||
Args:
|
||||
stride (bool): model with FPN need image shape % stride == 0
|
||||
"""
|
||||
|
||||
def __init__(self, stride=0):
|
||||
self.coarsest_stride = stride
|
||||
|
||||
def __call__(self, im, im_info):
|
||||
"""
|
||||
Args:
|
||||
im (np.ndarray): image (np.ndarray)
|
||||
im_info (dict): info of image
|
||||
Returns:
|
||||
im (np.ndarray): processed image (np.ndarray)
|
||||
im_info (dict): info of processed image
|
||||
"""
|
||||
coarsest_stride = self.coarsest_stride
|
||||
if coarsest_stride <= 0:
|
||||
return im, im_info
|
||||
im_c, im_h, im_w = im.shape
|
||||
pad_h = int(np.ceil(float(im_h) / coarsest_stride) * coarsest_stride)
|
||||
pad_w = int(np.ceil(float(im_w) / coarsest_stride) * coarsest_stride)
|
||||
padding_im = np.zeros((im_c, pad_h, pad_w), dtype=np.float32)
|
||||
padding_im[:, :im_h, :im_w] = im
|
||||
return padding_im, im_info
|
||||
|
||||
|
||||
class LetterBoxResize(object):
|
||||
def __init__(self, target_size):
|
||||
"""
|
||||
Resize image to target size, convert normalized xywh to pixel xyxy
|
||||
format ([x_center, y_center, width, height] -> [x0, y0, x1, y1]).
|
||||
Args:
|
||||
target_size (int|list): image target size.
|
||||
"""
|
||||
super(LetterBoxResize, self).__init__()
|
||||
if isinstance(target_size, int):
|
||||
target_size = [target_size, target_size]
|
||||
self.target_size = target_size
|
||||
|
||||
def letterbox(self, img, height, width, color=(127.5, 127.5, 127.5)):
|
||||
# letterbox: resize a rectangular image to a padded rectangular
|
||||
shape = img.shape[:2] # [height, width]
|
||||
ratio_h = float(height) / shape[0]
|
||||
ratio_w = float(width) / shape[1]
|
||||
ratio = min(ratio_h, ratio_w)
|
||||
new_shape = (round(shape[1] * ratio),
|
||||
round(shape[0] * ratio)) # [width, height]
|
||||
padw = (width - new_shape[0]) / 2
|
||||
padh = (height - new_shape[1]) / 2
|
||||
top, bottom = round(padh - 0.1), round(padh + 0.1)
|
||||
left, right = round(padw - 0.1), round(padw + 0.1)
|
||||
|
||||
img = cv2.resize(
|
||||
img, new_shape, interpolation=cv2.INTER_AREA) # resized, no border
|
||||
img = cv2.copyMakeBorder(
|
||||
img, top, bottom, left, right, cv2.BORDER_CONSTANT,
|
||||
value=color) # padded rectangular
|
||||
return img, ratio, padw, padh
|
||||
|
||||
def __call__(self, im, im_info):
|
||||
"""
|
||||
Args:
|
||||
im (np.ndarray): image (np.ndarray)
|
||||
im_info (dict): info of image
|
||||
Returns:
|
||||
im (np.ndarray): processed image (np.ndarray)
|
||||
im_info (dict): info of processed image
|
||||
"""
|
||||
assert len(self.target_size) == 2
|
||||
assert self.target_size[0] > 0 and self.target_size[1] > 0
|
||||
height, width = self.target_size
|
||||
h, w = im.shape[:2]
|
||||
im, ratio, padw, padh = self.letterbox(im, height=height, width=width)
|
||||
|
||||
new_shape = [round(h * ratio), round(w * ratio)]
|
||||
im_info['im_shape'] = np.array(new_shape, dtype=np.float32)
|
||||
im_info['scale_factor'] = np.array([ratio, ratio], dtype=np.float32)
|
||||
return im, im_info
|
||||
|
||||
|
||||
class Pad(object):
|
||||
def __init__(self, size, fill_value=[114.0, 114.0, 114.0]):
|
||||
"""
|
||||
Pad image to a specified size.
|
||||
Args:
|
||||
size (list[int]): image target size
|
||||
fill_value (list[float]): rgb value of pad area, default (114.0, 114.0, 114.0)
|
||||
"""
|
||||
super(Pad, self).__init__()
|
||||
if isinstance(size, int):
|
||||
size = [size, size]
|
||||
self.size = size
|
||||
self.fill_value = fill_value
|
||||
|
||||
def __call__(self, im, im_info):
|
||||
im_h, im_w = im.shape[:2]
|
||||
h, w = self.size
|
||||
if h == im_h and w == im_w:
|
||||
im = im.astype(np.float32)
|
||||
return im, im_info
|
||||
|
||||
canvas = np.ones((h, w, 3), dtype=np.float32)
|
||||
canvas *= np.array(self.fill_value, dtype=np.float32)
|
||||
canvas[0:im_h, 0:im_w, :] = im.astype(np.float32)
|
||||
im = canvas
|
||||
return im, im_info
|
||||
|
||||
|
||||
def rotate_point(pt, angle_rad):
|
||||
"""Rotate a point by an angle.
|
||||
|
||||
Args:
|
||||
pt (list[float]): 2 dimensional point to be rotated
|
||||
angle_rad (float): rotation angle by radian
|
||||
|
||||
Returns:
|
||||
list[float]: Rotated point.
|
||||
"""
|
||||
assert len(pt) == 2
|
||||
sn, cs = np.sin(angle_rad), np.cos(angle_rad)
|
||||
new_x = pt[0] * cs - pt[1] * sn
|
||||
new_y = pt[0] * sn + pt[1] * cs
|
||||
rotated_pt = [new_x, new_y]
|
||||
|
||||
return rotated_pt
|
||||
|
||||
|
||||
def _get_3rd_point(a, b):
|
||||
"""To calculate the affine matrix, three pairs of points are required. This
|
||||
function is used to get the 3rd point, given 2D points a & b.
|
||||
|
||||
The 3rd point is defined by rotating vector `a - b` by 90 degrees
|
||||
anticlockwise, using b as the rotation center.
|
||||
|
||||
Args:
|
||||
a (np.ndarray): point(x,y)
|
||||
b (np.ndarray): point(x,y)
|
||||
|
||||
Returns:
|
||||
np.ndarray: The 3rd point.
|
||||
"""
|
||||
assert len(a) == 2
|
||||
assert len(b) == 2
|
||||
direction = a - b
|
||||
third_pt = b + np.array([-direction[1], direction[0]], dtype=np.float32)
|
||||
|
||||
return third_pt
|
||||
|
||||
|
||||
def get_affine_transform(center,
|
||||
input_size,
|
||||
rot,
|
||||
output_size,
|
||||
shift=(0., 0.),
|
||||
inv=False):
|
||||
"""Get the affine transform matrix, given the center/scale/rot/output_size.
|
||||
|
||||
Args:
|
||||
center (np.ndarray[2, ]): Center of the bounding box (x, y).
|
||||
scale (np.ndarray[2, ]): Scale of the bounding box
|
||||
wrt [width, height].
|
||||
rot (float): Rotation angle (degree).
|
||||
output_size (np.ndarray[2, ]): Size of the destination heatmaps.
|
||||
shift (0-100%): Shift translation ratio wrt the width/height.
|
||||
Default (0., 0.).
|
||||
inv (bool): Option to inverse the affine transform direction.
|
||||
(inv=False: src->dst or inv=True: dst->src)
|
||||
|
||||
Returns:
|
||||
np.ndarray: The transform matrix.
|
||||
"""
|
||||
assert len(center) == 2
|
||||
assert len(output_size) == 2
|
||||
assert len(shift) == 2
|
||||
if not isinstance(input_size, (np.ndarray, list)):
|
||||
input_size = np.array([input_size, input_size], dtype=np.float32)
|
||||
scale_tmp = input_size
|
||||
|
||||
shift = np.array(shift)
|
||||
src_w = scale_tmp[0]
|
||||
dst_w = output_size[0]
|
||||
dst_h = output_size[1]
|
||||
|
||||
rot_rad = np.pi * rot / 180
|
||||
src_dir = rotate_point([0., src_w * -0.5], rot_rad)
|
||||
dst_dir = np.array([0., dst_w * -0.5])
|
||||
|
||||
src = np.zeros((3, 2), dtype=np.float32)
|
||||
src[0, :] = center + scale_tmp * shift
|
||||
src[1, :] = center + src_dir + scale_tmp * shift
|
||||
src[2, :] = _get_3rd_point(src[0, :], src[1, :])
|
||||
|
||||
dst = np.zeros((3, 2), dtype=np.float32)
|
||||
dst[0, :] = [dst_w * 0.5, dst_h * 0.5]
|
||||
dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir
|
||||
dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :])
|
||||
|
||||
if inv:
|
||||
trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))
|
||||
else:
|
||||
trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))
|
||||
|
||||
return trans
|
||||
|
||||
|
||||
class WarpAffine(object):
|
||||
"""Warp affine the image
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
keep_res=False,
|
||||
pad=31,
|
||||
input_h=512,
|
||||
input_w=512,
|
||||
scale=0.4,
|
||||
shift=0.1):
|
||||
self.keep_res = keep_res
|
||||
self.pad = pad
|
||||
self.input_h = input_h
|
||||
self.input_w = input_w
|
||||
self.scale = scale
|
||||
self.shift = shift
|
||||
|
||||
def __call__(self, im, im_info):
|
||||
"""
|
||||
Args:
|
||||
im (np.ndarray): image (np.ndarray)
|
||||
im_info (dict): info of image
|
||||
Returns:
|
||||
im (np.ndarray): processed image (np.ndarray)
|
||||
im_info (dict): info of processed image
|
||||
"""
|
||||
img = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
|
||||
|
||||
h, w = img.shape[:2]
|
||||
|
||||
if self.keep_res:
|
||||
input_h = (h | self.pad) + 1
|
||||
input_w = (w | self.pad) + 1
|
||||
s = np.array([input_w, input_h], dtype=np.float32)
|
||||
c = np.array([w // 2, h // 2], dtype=np.float32)
|
||||
|
||||
else:
|
||||
s = max(h, w) * 1.0
|
||||
input_h, input_w = self.input_h, self.input_w
|
||||
c = np.array([w / 2., h / 2.], dtype=np.float32)
|
||||
|
||||
trans_input = get_affine_transform(c, s, 0, [input_w, input_h])
|
||||
img = cv2.resize(img, (w, h))
|
||||
inp = cv2.warpAffine(
|
||||
img, trans_input, (input_w, input_h), flags=cv2.INTER_LINEAR)
|
||||
return inp, im_info
|
||||
|
||||
|
||||
# keypoint preprocess
|
||||
def get_warp_matrix(theta, size_input, size_dst, size_target):
|
||||
"""This code is based on
|
||||
https://github.com/open-mmlab/mmpose/blob/master/mmpose/core/post_processing/post_transforms.py
|
||||
|
||||
Calculate the transformation matrix under the constraint of unbiased.
|
||||
Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased
|
||||
Data Processing for Human Pose Estimation (CVPR 2020).
|
||||
|
||||
Args:
|
||||
theta (float): Rotation angle in degrees.
|
||||
size_input (np.ndarray): Size of input image [w, h].
|
||||
size_dst (np.ndarray): Size of output image [w, h].
|
||||
size_target (np.ndarray): Size of ROI in input plane [w, h].
|
||||
|
||||
Returns:
|
||||
matrix (np.ndarray): A matrix for transformation.
|
||||
"""
|
||||
theta = np.deg2rad(theta)
|
||||
matrix = np.zeros((2, 3), dtype=np.float32)
|
||||
scale_x = size_dst[0] / size_target[0]
|
||||
scale_y = size_dst[1] / size_target[1]
|
||||
matrix[0, 0] = np.cos(theta) * scale_x
|
||||
matrix[0, 1] = -np.sin(theta) * scale_x
|
||||
matrix[0, 2] = scale_x * (
|
||||
-0.5 * size_input[0] * np.cos(theta) + 0.5 * size_input[1] *
|
||||
np.sin(theta) + 0.5 * size_target[0])
|
||||
matrix[1, 0] = np.sin(theta) * scale_y
|
||||
matrix[1, 1] = np.cos(theta) * scale_y
|
||||
matrix[1, 2] = scale_y * (
|
||||
-0.5 * size_input[0] * np.sin(theta) - 0.5 * size_input[1] *
|
||||
np.cos(theta) + 0.5 * size_target[1])
|
||||
return matrix
|
||||
|
||||
|
||||
class TopDownEvalAffine(object):
|
||||
"""apply affine transform to image and coords
|
||||
|
||||
Args:
|
||||
trainsize (list): [w, h], the standard size used to train
|
||||
use_udp (bool): whether to use Unbiased Data Processing.
|
||||
records(dict): the dict contained the image and coords
|
||||
|
||||
Returns:
|
||||
records (dict): contain the image and coords after tranformed
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, trainsize, use_udp=False):
|
||||
self.trainsize = trainsize
|
||||
self.use_udp = use_udp
|
||||
|
||||
def __call__(self, image, im_info):
|
||||
rot = 0
|
||||
imshape = im_info['im_shape'][::-1]
|
||||
center = im_info['center'] if 'center' in im_info else imshape / 2.
|
||||
scale = im_info['scale'] if 'scale' in im_info else imshape
|
||||
if self.use_udp:
|
||||
trans = get_warp_matrix(
|
||||
rot, center * 2.0,
|
||||
[self.trainsize[0] - 1.0, self.trainsize[1] - 1.0], scale)
|
||||
image = cv2.warpAffine(
|
||||
image,
|
||||
trans, (int(self.trainsize[0]), int(self.trainsize[1])),
|
||||
flags=cv2.INTER_LINEAR)
|
||||
else:
|
||||
trans = get_affine_transform(center, scale, rot, self.trainsize)
|
||||
image = cv2.warpAffine(
|
||||
image,
|
||||
trans, (int(self.trainsize[0]), int(self.trainsize[1])),
|
||||
flags=cv2.INTER_LINEAR)
|
||||
|
||||
return image, im_info
|
||||
|
||||
|
||||
class Compose:
|
||||
def __init__(self, transforms):
|
||||
self.transforms = []
|
||||
for op_info in transforms:
|
||||
new_op_info = op_info.copy()
|
||||
op_type = new_op_info.pop('type')
|
||||
self.transforms.append(eval(op_type)(**new_op_info))
|
||||
|
||||
def __call__(self, img):
|
||||
img, im_info = decode_image(img)
|
||||
for t in self.transforms:
|
||||
img, im_info = t(img, im_info)
|
||||
inputs = copy.deepcopy(im_info)
|
||||
inputs['image'] = img
|
||||
return inputs
|
||||
261
paddle_detection/deploy/serving/python/web_service.py
Normal file
261
paddle_detection/deploy/serving/python/web_service.py
Normal file
@@ -0,0 +1,261 @@
|
||||
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
import copy
|
||||
|
||||
from paddle_serving_server.web_service import WebService, Op
|
||||
from paddle_serving_server.proto import general_model_config_pb2 as m_config
|
||||
import google.protobuf.text_format
|
||||
|
||||
import os
|
||||
import numpy as np
|
||||
import base64
|
||||
from PIL import Image
|
||||
import io
|
||||
from preprocess_ops import Compose
|
||||
from postprocess_ops import HRNetPostProcess
|
||||
|
||||
from argparse import ArgumentParser, RawDescriptionHelpFormatter
|
||||
import yaml
|
||||
|
||||
# Global dictionary
|
||||
SUPPORT_MODELS = {
|
||||
'YOLO', 'PPYOLOE', 'RCNN', 'SSD', 'Face', 'FCOS', 'SOLOv2', 'TTFNet',
|
||||
'S2ANet', 'JDE', 'FairMOT', 'DeepSORT', 'GFL', 'PicoDet', 'CenterNet',
|
||||
'TOOD', 'RetinaNet', 'StrongBaseline', 'STGCN', 'YOLOX', 'HRNet'
|
||||
}
|
||||
|
||||
GLOBAL_VAR = {}
|
||||
|
||||
|
||||
class ArgsParser(ArgumentParser):
|
||||
def __init__(self):
|
||||
super(ArgsParser, self).__init__(
|
||||
formatter_class=RawDescriptionHelpFormatter)
|
||||
self.add_argument(
|
||||
"-c",
|
||||
"--config",
|
||||
default="deploy/serving/python/config.yml",
|
||||
help="configuration file to use")
|
||||
self.add_argument(
|
||||
"--model_dir",
|
||||
type=str,
|
||||
default=None,
|
||||
help=("Directory include:'model.pdiparams', 'model.pdmodel', "
|
||||
"'infer_cfg.yml', created by tools/export_model.py."),
|
||||
required=True)
|
||||
self.add_argument(
|
||||
"-o", "--opt", nargs='+', help="set configuration options")
|
||||
|
||||
def parse_args(self, argv=None):
|
||||
args = super(ArgsParser, self).parse_args(argv)
|
||||
assert args.config is not None, \
|
||||
"Please specify --config=configure_file_path."
|
||||
args.service_config = self._parse_opt(args.opt, args.config)
|
||||
args.model_config = PredictConfig(args.model_dir)
|
||||
return args
|
||||
|
||||
def _parse_helper(self, v):
|
||||
if v.isnumeric():
|
||||
if "." in v:
|
||||
v = float(v)
|
||||
else:
|
||||
v = int(v)
|
||||
elif v == "True" or v == "False":
|
||||
v = (v == "True")
|
||||
return v
|
||||
|
||||
def _parse_opt(self, opts, conf_path):
|
||||
f = open(conf_path)
|
||||
config = yaml.load(f, Loader=yaml.Loader)
|
||||
if not opts:
|
||||
return config
|
||||
for s in opts:
|
||||
s = s.strip()
|
||||
k, v = s.split('=')
|
||||
v = self._parse_helper(v)
|
||||
if "devices" in k:
|
||||
v = str(v)
|
||||
print(k, v, type(v))
|
||||
cur = config
|
||||
parent = cur
|
||||
for kk in k.split("."):
|
||||
if kk not in cur:
|
||||
cur[kk] = {}
|
||||
parent = cur
|
||||
cur = cur[kk]
|
||||
else:
|
||||
parent = cur
|
||||
cur = cur[kk]
|
||||
parent[k.split(".")[-1]] = v
|
||||
return config
|
||||
|
||||
|
||||
class PredictConfig(object):
|
||||
"""set config of preprocess, postprocess and visualize
|
||||
Args:
|
||||
model_dir (str): root path of infer_cfg.yml
|
||||
"""
|
||||
|
||||
def __init__(self, model_dir):
|
||||
# parsing Yaml config for Preprocess
|
||||
deploy_file = os.path.join(model_dir, 'infer_cfg.yml')
|
||||
with open(deploy_file) as f:
|
||||
yml_conf = yaml.safe_load(f)
|
||||
self.check_model(yml_conf)
|
||||
self.arch = yml_conf['arch']
|
||||
self.preprocess_infos = yml_conf['Preprocess']
|
||||
self.min_subgraph_size = yml_conf['min_subgraph_size']
|
||||
self.label_list = yml_conf['label_list']
|
||||
self.use_dynamic_shape = yml_conf['use_dynamic_shape']
|
||||
self.draw_threshold = yml_conf.get("draw_threshold", 0.5)
|
||||
self.mask = yml_conf.get("mask", False)
|
||||
self.tracker = yml_conf.get("tracker", None)
|
||||
self.nms = yml_conf.get("NMS", None)
|
||||
self.fpn_stride = yml_conf.get("fpn_stride", None)
|
||||
if self.arch == 'RCNN' and yml_conf.get('export_onnx', False):
|
||||
print(
|
||||
'The RCNN export model is used for ONNX and it only supports batch_size = 1'
|
||||
)
|
||||
self.print_config()
|
||||
|
||||
def check_model(self, yml_conf):
|
||||
"""
|
||||
Raises:
|
||||
ValueError: loaded model not in supported model type
|
||||
"""
|
||||
for support_model in SUPPORT_MODELS:
|
||||
if support_model in yml_conf['arch']:
|
||||
return True
|
||||
raise ValueError("Unsupported arch: {}, expect {}".format(yml_conf[
|
||||
'arch'], SUPPORT_MODELS))
|
||||
|
||||
def print_config(self):
|
||||
print('----------- Model Configuration -----------')
|
||||
print('%s: %s' % ('Model Arch', self.arch))
|
||||
print('%s: ' % ('Transform Order'))
|
||||
for op_info in self.preprocess_infos:
|
||||
print('--%s: %s' % ('transform op', op_info['type']))
|
||||
print('--------------------------------------------')
|
||||
|
||||
|
||||
class DetectorOp(Op):
|
||||
def init_op(self):
|
||||
self.preprocess_pipeline = Compose(GLOBAL_VAR['preprocess_ops'])
|
||||
|
||||
def preprocess(self, input_dicts, data_id, log_id):
|
||||
(_, input_dict), = input_dicts.items()
|
||||
inputs = []
|
||||
for key, data in input_dict.items():
|
||||
data = base64.b64decode(data.encode('utf8'))
|
||||
byte_stream = io.BytesIO(data)
|
||||
img = Image.open(byte_stream).convert("RGB")
|
||||
inputs.append(self.preprocess_pipeline(img))
|
||||
inputs = self.collate_inputs(inputs)
|
||||
return inputs, False, None, ""
|
||||
|
||||
def postprocess(self, input_dicts, fetch_dict, data_id, log_id):
|
||||
(_, input_dict), = input_dicts.items()
|
||||
if GLOBAL_VAR['model_config'].arch in ["HRNet"]:
|
||||
result = self.parse_keypoint_result(input_dict, fetch_dict)
|
||||
else:
|
||||
result = self.parse_detection_result(input_dict, fetch_dict)
|
||||
return result, None, ""
|
||||
|
||||
def collate_inputs(self, inputs):
|
||||
collate_inputs = {k: [] for k in inputs[0].keys()}
|
||||
for info in inputs:
|
||||
for k in collate_inputs.keys():
|
||||
collate_inputs[k].append(info[k])
|
||||
return {
|
||||
k: np.stack(v)
|
||||
for k, v in collate_inputs.items() if k in GLOBAL_VAR['feed_vars']
|
||||
}
|
||||
|
||||
def parse_detection_result(self, input_dict, fetch_dict):
|
||||
bboxes = fetch_dict[GLOBAL_VAR['fetch_vars'][0]]
|
||||
bboxes_num = fetch_dict[GLOBAL_VAR['fetch_vars'][1]]
|
||||
if GLOBAL_VAR['model_config'].mask:
|
||||
masks = fetch_dict[GLOBAL_VAR['fetch_vars'][2]]
|
||||
idx = 0
|
||||
results = {}
|
||||
for img_name, num in zip(input_dict.keys(), bboxes_num):
|
||||
if num == 0:
|
||||
results[img_name] = 'No object detected!'
|
||||
else:
|
||||
result = []
|
||||
bbox = bboxes[idx:idx + num]
|
||||
for line in bbox:
|
||||
if line[0] > -1 and line[1] > GLOBAL_VAR[
|
||||
'model_config'].draw_threshold:
|
||||
result.append(
|
||||
f"{int(line[0])} {line[1]} "
|
||||
f"{line[2]} {line[3]} {line[4]} {line[5]}")
|
||||
if len(result) == 0:
|
||||
result = 'No object detected!'
|
||||
results[img_name] = result
|
||||
idx += num
|
||||
return results
|
||||
|
||||
def parse_keypoint_result(self, input_dict, fetch_dict):
|
||||
heatmap = fetch_dict["conv2d_441.tmp_1"]
|
||||
keypoint_postprocess = HRNetPostProcess()
|
||||
im_shape = []
|
||||
for key, data in input_dict.items():
|
||||
data = base64.b64decode(data.encode('utf8'))
|
||||
byte_stream = io.BytesIO(data)
|
||||
img = Image.open(byte_stream).convert("RGB")
|
||||
im_shape.append([img.width, img.height])
|
||||
im_shape = np.array(im_shape)
|
||||
center = np.round(im_shape / 2.)
|
||||
scale = im_shape / 200.
|
||||
kpts, scores = keypoint_postprocess(heatmap, center, scale)
|
||||
results = {"keypoint": kpts, "scores": scores}
|
||||
return results
|
||||
|
||||
|
||||
class DetectorService(WebService):
|
||||
def get_pipeline_response(self, read_op):
|
||||
return DetectorOp(name="ppdet", input_ops=[read_op])
|
||||
|
||||
|
||||
def get_model_vars(model_dir, service_config):
|
||||
serving_server_dir = os.path.join(model_dir, "serving_server")
|
||||
# rewrite model_config
|
||||
service_config['op']['ppdet']['local_service_conf'][
|
||||
'model_config'] = serving_server_dir
|
||||
serving_server_conf = os.path.join(serving_server_dir,
|
||||
"serving_server_conf.prototxt")
|
||||
with open(serving_server_conf, 'r') as f:
|
||||
model_var = google.protobuf.text_format.Merge(
|
||||
str(f.read()), m_config.GeneralModelConfig())
|
||||
feed_vars = [var.name for var in model_var.feed_var]
|
||||
fetch_vars = [var.name for var in model_var.fetch_var]
|
||||
return feed_vars, fetch_vars
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# load config and prepare the service
|
||||
FLAGS = ArgsParser().parse_args()
|
||||
feed_vars, fetch_vars = get_model_vars(FLAGS.model_dir,
|
||||
FLAGS.service_config)
|
||||
GLOBAL_VAR['feed_vars'] = feed_vars
|
||||
GLOBAL_VAR['fetch_vars'] = fetch_vars
|
||||
GLOBAL_VAR['preprocess_ops'] = FLAGS.model_config.preprocess_infos
|
||||
GLOBAL_VAR['model_config'] = FLAGS.model_config
|
||||
print(FLAGS)
|
||||
# define the service
|
||||
uci_service = DetectorService(name="ppdet")
|
||||
uci_service.prepare_pipeline_config(yml_dict=FLAGS.service_config)
|
||||
# start the service
|
||||
uci_service.run_service()
|
||||
43
paddle_detection/deploy/serving/test_client.py
Normal file
43
paddle_detection/deploy/serving/test_client.py
Normal file
@@ -0,0 +1,43 @@
|
||||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import sys
|
||||
import numpy as np
|
||||
from paddle_serving_client import Client
|
||||
from paddle_serving_app.reader import *
|
||||
import cv2
|
||||
preprocess = Sequential([
|
||||
File2Image(), BGR2RGB(), Resize(
|
||||
(608, 608), interpolation=cv2.INTER_LINEAR), Div(255.0), Transpose(
|
||||
(2, 0, 1))
|
||||
])
|
||||
|
||||
postprocess = RCNNPostprocess(sys.argv[1], "output", [608, 608])
|
||||
client = Client()
|
||||
|
||||
client.load_client_config("serving_client/serving_client_conf.prototxt")
|
||||
client.connect(['127.0.0.1:9393'])
|
||||
|
||||
im = preprocess(sys.argv[2])
|
||||
fetch_map = client.predict(
|
||||
feed={
|
||||
"image": im,
|
||||
"im_shape": np.array(list(im.shape[1:])).reshape(-1),
|
||||
"scale_factor": np.array([1.0, 1.0]).reshape(-1),
|
||||
},
|
||||
fetch=["multiclass_nms3_0.tmp_0"],
|
||||
batch=False)
|
||||
print(fetch_map)
|
||||
fetch_map["image"] = sys.argv[2]
|
||||
postprocess(fetch_map)
|
||||
Reference in New Issue
Block a user