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95 lines
4.1 KiB
Markdown
Executable File
95 lines
4.1 KiB
Markdown
Executable File
English | [简体中文](README_CN.md)
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# PP-OCR Serving Deployment Example
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Before the serving deployment, please confirm
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- 1. Refer to [FastDeploy Serving Deployment](../../../../../serving/README.md) for software and hardware environment requirements and image pull commands
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## Introduction
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This document describes how to build an OCR text recognition service with FastDeploy.
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The server must be started in docker, while the client does not need to be in a docker container.
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**The models in the path ($PWD) contain the model configuration and code (the server will load the models and code to start the service), which needs to be mapped to docker.**
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OCR consists of det (detection), cls (classification) and rec (recognition) models.
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The diagram of the serving deployment is shown below, where `pp_ocr` connects to `det_preprocess`、`det_runtime` and `det_postprocess`,`cls_pp` connects to `cls_runtime` and `cls_postprocess`,`rec_pp` connects to `rec_runtime` and `rec_postprocess`.
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In particular, `cls_pp` and `rec_pp` services are called multiple times in `det_postprocess` to realize the classification and identification of the detection results (multiple boxes), and finally return the identification results to users.
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<p align="center">
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<br>
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<img src='./ppocr.png'">
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<br>
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<p>
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## Usage
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### 1. Server
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#### 1.1 Docker
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```bash
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# Download the repository code
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/ocr/PP-OCRv3/serving/
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# Dpwnload model, image, and dictionary files
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wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
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tar xvf ch_PP-OCRv3_det_infer.tar && mv ch_PP-OCRv3_det_infer 1
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mv 1/inference.pdiparams 1/model.pdiparams && mv 1/inference.pdmodel 1/model.pdmodel
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mv 1 models/det_runtime/ && rm -rf ch_PP-OCRv3_det_infer.tar
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wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
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tar xvf ch_ppocr_mobile_v2.0_cls_infer.tar && mv ch_ppocr_mobile_v2.0_cls_infer 1
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mv 1/inference.pdiparams 1/model.pdiparams && mv 1/inference.pdmodel 1/model.pdmodel
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mv 1 models/cls_runtime/ && rm -rf ch_ppocr_mobile_v2.0_cls_infer.tar
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wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
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tar xvf ch_PP-OCRv3_rec_infer.tar && mv ch_PP-OCRv3_rec_infer 1
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mv 1/inference.pdiparams 1/model.pdiparams && mv 1/inference.pdmodel 1/model.pdmodel
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mv 1 models/rec_runtime/ && rm -rf ch_PP-OCRv3_rec_infer.tar
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mkdir models/pp_ocr/1 && mkdir models/rec_pp/1 && mkdir models/cls_pp/1
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wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt
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mv ppocr_keys_v1.txt models/rec_postprocess/1/
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wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg
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# x.y.z represent the image version. Refer to serving document to replace them with numbers
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docker pull registry.baidubce.com/paddlepaddle/fastdeploy:x.y.z-gpu-cuda11.4-trt8.4-21.10
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docker run -dit --net=host --name fastdeploy --shm-size="1g" -v $PWD:/ocr_serving registry.baidubce.com/paddlepaddle/fastdeploy:x.y.z-gpu-cuda11.4-trt8.4-21.10 bash
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docker exec -it -u root fastdeploy bash
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```
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#### 1.2 Installation (in docker)
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```bash
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ldconfig
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apt-get install libgl1
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```
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#### 1.3 Start the server (in docker)
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```bash
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fastdeployserver --model-repository=/ocr_serving/models
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```
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Parameter:
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- `model-repository`(required): The storage path of the entire model streaming_pp_tts.
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- `http-port`(optional): Port number for the HTTP service. Default: `8000`. This port is not used in this example.
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- `grpc-port`(optional): Port number for the GRPC service. Default: `8001`.
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- `metrics-port`(optional): Port number for the serer metric. Default: `8002`. This port is not used in this example.
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### 2. Client
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#### 2.1 Installation
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```bash
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pip3 install tritonclient[all]
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```
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#### 2.2 Send Requests
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```bash
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python3 client.py
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```
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## Configuration Change
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The current default configuration runs on GPU. If you want to run it on CPU or other inference engines, please modify the configuration in `models/runtime/config.pbtxt`. Refer to [Configuration Document](../../../../../serving/docs/EN/model_configuration-en.md) for more information.
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