Update README.md
Bin Lu
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# OCR Pipeline WebService
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# PaddleClas Pipeline WebService
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(English|[简体中文](./README_CN.md))
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PaddleOCR provides two service deployment methods:
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PaddleClas provides two service deployment methods:
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- Based on **PaddleHub Serving**: Code path is "`./deploy/hubserving`". Please refer to the [tutorial](../../deploy/hubserving/readme_en.md)
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- Based on **PaddleServing**: Code path is "`./deploy/pdserving`". Please follow this tutorial.
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- Based on **PaddleServing**: Code path is "`./deploy/paddleserving`". Please follow this tutorial.
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# Service deployment based on PaddleServing
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@ -27,11 +27,10 @@ The introduction and tutorial of Paddle Serving service deployment framework ref
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<a name="environmental-preparation"></a>
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## Environmental preparation
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PaddleOCR operating environment and Paddle Serving operating environment are needed.
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1. Please prepare PaddleOCR operating environment reference [link](../../doc/doc_ch/installation.md).
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Download the corresponding paddle whl package according to the environment, it is recommended to install version 2.0.1.
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PaddleClas operating environment and PaddleServing operating environment are needed.
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1. Please prepare PaddleClas operating environment reference [link](../../docs/zh_CN/tutorials/install.md).
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Download the corresponding paddle whl package according to the environment, it is recommended to install version 2.1.0.
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2. The steps of PaddleServing operating environment prepare are as follows:
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@ -65,71 +64,80 @@ PaddleOCR operating environment and Paddle Serving operating environment are nee
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## Model conversion
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When using PaddleServing for service deployment, you need to convert the saved inference model into a serving model that is easy to deploy.
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Firstly, download the [inference model](https://github.com/PaddlePaddle/PaddleOCR#pp-ocr-20-series-model-listupdate-on-dec-15) of PPOCR
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Firstly, download the inference model of ResNet50_vd
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```
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# Download and unzip the ResNet50_vd model
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wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_infer.tar && tar xf ResNet50_vd_infer.tar
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```
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# Download and unzip the OCR text detection model
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wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar
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# Download and unzip the OCR text recognition model
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wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
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```
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Then, you can use installed paddle_serving_client tool to convert inference model to mobile model.
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```
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# Detection model conversion
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python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_mobile_v2.0_det_infer/ \
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--model_filename inference.pdmodel \
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--params_filename inference.pdiparams \
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--serving_server ./ppocr_det_mobile_2.0_serving/ \
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--serving_client ./ppocr_det_mobile_2.0_client/
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# Recognition model conversion
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python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_mobile_v2.0_rec_infer/ \
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--model_filename inference.pdmodel \
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--params_filename inference.pdiparams \
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--serving_server ./ppocr_rec_mobile_2.0_serving/ \
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--serving_client ./ppocr_rec_mobile_2.0_client/
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# ResNet50_vd model conversion
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python3 -m paddle_serving_client.convert --dirname ./ResNet50_vd_infer/ \
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--model_filename inference.pdmodel \
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--params_filename inference.pdiparams \
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--serving_server ./ResNet50_vd_serving/ \
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--serving_client ./ResNet50_vd_client/
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```
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After the detection model is converted, there will be additional folders of `ppocr_det_mobile_2.0_serving` and `ppocr_det_mobile_2.0_client` in the current folder, with the following format:
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After the ResNet50_vd inference model is converted, there will be additional folders of `ResNet50_vd_serving` and `ResNet50_vd_client` in the current folder, with the following format:
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```
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|- ppocr_det_mobile_2.0_serving/
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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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|- ppocr_det_mobile_2.0_client
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|- serving_client_conf.prototxt
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|- serving_client_conf.stream.prototxt
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|- ResNet50_vd_client/
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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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|- ResNet50_vd_client
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|- serving_client_conf.prototxt
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|- serving_client_conf.stream.prototxt
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```
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Once you have the deploy model file, you need to change the alias name in serving_server_conf.prototxt: Change 'alias_name' in 'feed_var' to 'image', change 'alias_name' in 'fetch_var' to 'prediction',
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The modified serving_server_conf.prototxt file is as follows:
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```
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feed_var {
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name: "inputs"
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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: 224
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shape: 224
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}
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fetch_var {
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name: "save_infer_model/scale_0.tmp_1"
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alias_name: "prediction"
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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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```
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The recognition model is the same.
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<a name="paddle-serving-pipeline-deployment"></a>
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## Paddle Serving pipeline deployment
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1. Download the PaddleOCR code, if you have already downloaded it, you can skip this step.
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1. Download the PaddleClas code, if you have already downloaded it, you can skip this step.
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```
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git clone https://github.com/PaddlePaddle/PaddleOCR
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git clone https://github.com/PaddlePaddle/PaddleClas
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# Enter the working directory
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cd PaddleOCR/deploy/pdserver/
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cd PaddleClas/deploy/paddleserving/
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```
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The pdserver directory contains the code to start the pipeline service and send prediction requests, including:
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The paddleserving directory contains the code to start the pipeline service and send prediction requests, including:
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```
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__init__.py
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config.yml # Start the service configuration file
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ocr_reader.py # OCR model pre-processing and post-processing code implementation
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pipeline_http_client.py # Script to send pipeline prediction request
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web_service.py # Start the script of the pipeline server
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config.yml # configuration file of starting the service
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pipeline_http_client.py # script to send pipeline prediction request by http
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pipeline_rpc_client.py # script to send pipeline prediction request by rpc
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resnet50_web_service.py # start the script of the pipeline server
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```
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2. Run the following command to start the service.
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```
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# Start the service and save the running log in log.txt
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python3 web_service.py &>log.txt &
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python3 resnet50_web_service.py &>log.txt &
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```
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After the service is successfully started, a log similar to the following will be printed in log.txt
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@ -156,74 +164,6 @@ The recognition model is the same.
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The predicted performance data will be automatically written into the `PipelineServingLogs/pipeline.tracer` file.
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Tested on 200 real pictures, and limited the detection long side to 960. The average QPS on T4 GPU can reach around 23:
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```
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2021-05-13 03:42:36,895 ==================== TRACER ======================
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2021-05-13 03:42:36,975 Op(rec):
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2021-05-13 03:42:36,976 in[14.472382882882883 ms]
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2021-05-13 03:42:36,976 prep[9.556855855855856 ms]
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2021-05-13 03:42:36,976 midp[59.921905405405404 ms]
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2021-05-13 03:42:36,976 postp[15.345945945945946 ms]
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2021-05-13 03:42:36,976 out[1.9921216216216215 ms]
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2021-05-13 03:42:36,976 idle[0.16254943864471572]
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2021-05-13 03:42:36,976 Op(det):
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2021-05-13 03:42:36,976 in[315.4468035714286 ms]
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2021-05-13 03:42:36,976 prep[69.5980625 ms]
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2021-05-13 03:42:36,976 midp[18.989535714285715 ms]
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2021-05-13 03:42:36,976 postp[18.857803571428573 ms]
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2021-05-13 03:42:36,977 out[3.1337544642857145 ms]
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2021-05-13 03:42:36,977 idle[0.7477961159203756]
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2021-05-13 03:42:36,977 DAGExecutor:
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2021-05-13 03:42:36,977 Query count[224]
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2021-05-13 03:42:36,977 QPS[22.4 q/s]
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2021-05-13 03:42:36,977 Succ[0.9910714285714286]
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2021-05-13 03:42:36,977 Error req[169, 170]
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2021-05-13 03:42:36,977 Latency:
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2021-05-13 03:42:36,977 ave[535.1678348214285 ms]
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2021-05-13 03:42:36,977 .50[172.651 ms]
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2021-05-13 03:42:36,977 .60[187.904 ms]
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2021-05-13 03:42:36,977 .70[245.675 ms]
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2021-05-13 03:42:36,977 .80[526.684 ms]
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2021-05-13 03:42:36,977 .90[854.596 ms]
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2021-05-13 03:42:36,977 .95[1722.728 ms]
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2021-05-13 03:42:36,977 .99[3990.292 ms]
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2021-05-13 03:42:36,978 Channel (server worker num[10]):
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2021-05-13 03:42:36,978 chl0(In: ['@DAGExecutor'], Out: ['det']) size[0/0]
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2021-05-13 03:42:36,979 chl1(In: ['det'], Out: ['rec']) size[6/0]
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2021-05-13 03:42:36,979 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
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```
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## WINDOWS Users
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Windows does not support Pipeline Serving, if we want to lauch paddle serving on Windows, we should use Web Service, for more infomation please refer to [Paddle Serving for Windows Users](https://github.com/PaddlePaddle/Serving/blob/develop/doc/WINDOWS_TUTORIAL.md)
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**WINDOWS user can only use version 0.5.0 CPU Mode**
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**Prepare Stage:**
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```
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pip3 install paddle-serving-server==0.5.0
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pip3 install paddle-serving-app==0.3.1
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```
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1. Start Server
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```
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cd win
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python3 ocr_web_server.py gpu(for gpu user)
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or
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python3 ocr_web_server.py cpu(for cpu user)
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```
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2. Client Send Requests
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```
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python3 ocr_web_client.py
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```
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<a name="faq"></a>
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## FAQ
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**Q1**: No result return after sending the request.
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