From 6077d952a9e3eaf5c958259a0425bb8ed7c9b8d8 Mon Sep 17 00:00:00 2001 From: tink2123 Date: Fri, 8 Apr 2022 03:25:08 +0000 Subject: [PATCH] update readme, return bbox --- deploy/pdserving/README.md | 29 +++++++++++++++++++++--- deploy/pdserving/README_CN.md | 36 ++++++++++++++++++++++++++---- deploy/pdserving/ocr_cpp_client.py | 2 -- deploy/pdserving/web_service.py | 31 ++++++++++++++++--------- 4 files changed, 78 insertions(+), 20 deletions(-) diff --git a/deploy/pdserving/README.md b/deploy/pdserving/README.md index 7ed52af9..6c701eda 100644 --- a/deploy/pdserving/README.md +++ b/deploy/pdserving/README.md @@ -31,8 +31,6 @@ PaddleOCR operating environment and Paddle Serving operating environment are nee 1. Please prepare PaddleOCR operating environment reference [link](../../doc/doc_ch/installation.md). - Download the corresponding paddle whl package according to the environment, it is recommended to install version 2.2.2 - 2. The steps of PaddleServing operating environment prepare are as follows: @@ -191,6 +189,15 @@ The recognition model is the same. ``` ## C++ Serving +Service deployment based on python obviously has the advantage of convenient secondary development. However, the real application often needs to pursue better performance. PaddleServing also provides a more performant C++ deployment version. + +The C++ service deployment is the same as python in the environment setup and data preparation stages, the difference is when the service is started and the client sends requests. + +| Language | Speed ​​| Secondary development | Do you need to compile | +|-----|-----|---------|------------| +| C++ | fast | Slightly difficult | Single model prediction does not need to be compiled, multi-model concatenation needs to be compiled | +| python | general | easy | single-model/multi-model no compilation required | + 1. Compile Serving To improve predictive performance, C++ services also provide multiple model concatenation services. Unlike Python Pipeline services, multiple model concatenation requires the pre - and post-model processing code to be written on the server side, so local recompilation is required to generate serving. Specific may refer to the official document: [how to compile Serving](https://github.com/PaddlePaddle/Serving/blob/v0.8.3/doc/Compile_EN.md) @@ -198,12 +205,28 @@ The recognition model is the same. 2. Run the following command to start the service. ``` # Start the service and save the running log in log.txt - python3 -m paddle_serving_server.serve --model ppocrv2_det_serving ppocrv2_rec_serving --op GeneralDetectionOp GeneralRecOp --port 9293 &>log.txt & + python3 -m paddle_serving_server.serve --model ppocrv2_det_serving ppocrv2_rec_serving --op GeneralDetectionOp GeneralInferOp --port 9293 &>log.txt & ``` After the service is successfully started, a log similar to the following will be printed in log.txt ![](./imgs/start_server.png) 3. Send service request + + Due to the need for pre and post-processing in the C++Server part, in order to speed up the input to the C++Server is only the base64 encoded string of the picture, it needs to be manually modified + Change the feed_type field and shape field in ppocrv2_det_client/serving_client_conf.prototxt to the following: + + ``` + feed_var { + name: "x" + alias_name: "x" + is_lod_tensor: false + feed_type: 20 + shape: 1 + } + ``` + + start the client: + ``` python3 ocr_cpp_client.py ppocrv2_det_client ppocrv2_rec_client ``` diff --git a/deploy/pdserving/README_CN.md b/deploy/pdserving/README_CN.md index aad9e14e..f3f083d1 100644 --- a/deploy/pdserving/README_CN.md +++ b/deploy/pdserving/README_CN.md @@ -6,6 +6,8 @@ PaddleOCR提供2种服务部署方式： - 基于PaddleHub Serving的部署：代码路径为"`./deploy/hubserving`"，使用方法参考[文档](../../deploy/hubserving/readme.md)； - 基于PaddleServing的部署：代码路径为"`./deploy/pdserving`"，按照本教程使用。 +* AIStudio演示案例可参考 [基于PaddleServing的OCR服务化部署实战](https://aistudio.baidu.com/aistudio/projectdetail/3630726) + # 基于PaddleServing的服务部署 本文档将介绍如何使用[PaddleServing](https://github.com/PaddlePaddle/Serving/blob/develop/README_CN.md)工具部署PP-OCR动态图模型的pipeline在线服务。 @@ -32,8 +34,6 @@ PaddleOCR提供2种服务部署方式： - 准备PaddleOCR的运行环境[链接](../../doc/doc_ch/installation.md) - 根据环境下载对应的paddlepaddle whl包，推荐安装2.2.2版本 - - 准备PaddleServing的运行环境，步骤如下 ```bash @@ -197,9 +197,24 @@ python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_rec_infer/ \ C++ 服务部署在环境搭建和数据准备阶段与 python 相同，区别在于启动服务和客户端发送请求时不同。 +| 语言 | 速度 | 二次开发 | 是否需要编译 | +|-----|-----|---------|------------| +| C++ | 很快 | 略有难度 | 单模型预测无需编译，多模型串联需要编译 | +| python | 一般 ｜ 容易 | 单模型/多模型 均无需编译| + 1. 准备 Serving 环境 -为了提高预测性能，C++ 服务同样提供了多模型串联服务。与python pipeline服务不同，多模型串联的过程中需要将模型前后处理代码写在服务端，因此需要在本地重新编译生成serving。具体可参考官方文档：[如何编译Serving](https://github.com/PaddlePaddle/Serving/blob/v0.8.3/doc/Compile_CN.md) +为了提高预测性能，C++ 服务同样提供了多模型串联服务。与python pipeline服务不同，多模型串联的过程中需要将模型前后处理代码写在服务端，因此需要在本地重新编译生成serving。 + +首先需要下载Serving代码库, 把OCR文本检测预处理相关代码替换到Serving库中 +``` +git clone https://github.com/PaddlePaddle/Serving + +cp -rf general_detection_op.cpp Serving/core/general-server/op + +``` + +具体可参考官方文档：[如何编译Serving](https://github.com/PaddlePaddle/Serving/blob/v0.8.3/doc/Compile_CN.md)，注意需要开启 WITH_OPENCV 选项。 完成编译后，注意要安装编译出的三个whl包，并设置SERVING_BIN环境变量。 @@ -209,12 +224,25 @@ C++ 服务部署在环境搭建和数据准备阶段与 python 相同，区别 ``` # 启动服务，运行日志保存在log.txt - python3 -m paddle_serving_server.serve --model ppocrv2_det_serving ppocrv2_rec_serving --op GeneralDetectionOp GeneralRecOp --port 9293 &>log.txt & + python3 -m paddle_serving_server.serve --model ppocrv2_det_serving ppocrv2_rec_serving --op GeneralDetectionOp GeneralInferOp --port 9293 &>log.txt & ``` 成功启动服务后，log.txt中会打印类似如下日志 ![](./imgs/start_server.png) 3. 发送服务请求： + + 由于需要在C++Server部分进行前后处理，为了加速传入C++Server的仅仅是图片的base64编码的字符串，故需要手动修改 + ppocrv2_det_client/serving_client_conf.prototxt 中 feed_type 字段 和 shape 字段，修改成如下内容： + ``` + feed_var { + name: "x" + alias_name: "x" + is_lod_tensor: false + feed_type: 20 + shape: 1 + } + ``` + 启动客户端 ``` python3 ocr_cpp_client.py ppocrv2_det_client ppocrv2_rec_client ``` diff --git a/deploy/pdserving/ocr_cpp_client.py b/deploy/pdserving/ocr_cpp_client.py index 21c5537f..415ba4ea 100755 --- a/deploy/pdserving/ocr_cpp_client.py +++ b/deploy/pdserving/ocr_cpp_client.py @@ -47,9 +47,7 @@ for img_file in os.listdir(test_img_dir): res_list = [] fetch_map = client.predict( feed={"x": image}, fetch=["save_infer_model/scale_0.tmp_1"], batch=True) - print("fetrch map:", fetch_map) one_batch_res = ocr_reader.postprocess(fetch_map, with_score=True) for res in one_batch_res: - res_list.append(res[0]) res = {"res": str(res_list)} print(res) diff --git a/deploy/pdserving/web_service.py b/deploy/pdserving/web_service.py index b97c6e1f..74597e44 100644 --- a/deploy/pdserving/web_service.py +++ b/deploy/pdserving/web_service.py @@ -15,6 +15,7 @@ from paddle_serving_server.web_service import WebService, Op import logging import numpy as np +import copy import cv2 import base64 # from paddle_serving_app.reader import OCRReader @@ -29,14 +30,16 @@ _LOGGER = logging.getLogger() class DetOp(Op): def init_op(self): self.det_preprocess = Sequential([ - DetResizeForTest(), Div(255), - Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose( + DetResizeForTest(), + Div(255), + Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), + Transpose( (2, 0, 1)) ]) self.filter_func = FilterBoxes(10, 10) self.post_func = DBPostProcess({ "thresh": 0.3, - "box_thresh": 0.5, + "box_thresh": 0.6, "max_candidates": 1000, "unclip_ratio": 1.5, "min_size": 3 @@ -79,8 +82,10 @@ class RecOp(Op): raw_im = input_dict["image"] data = np.frombuffer(raw_im, np.uint8) im = cv2.imdecode(data, cv2.IMREAD_COLOR) - dt_boxes = input_dict["dt_boxes"] - dt_boxes = self.sorted_boxes(dt_boxes) + self.dt_list = input_dict["dt_boxes"] + self.dt_list = self.sorted_boxes(self.dt_list) + # deepcopy to save origin dt_boxes + dt_boxes = copy.deepcopy(self.dt_list) feed_list = [] img_list = [] max_wh_ratio = 0 @@ -126,25 +131,29 @@ class RecOp(Op): imgs[id] = norm_img feed = {"x": imgs.copy()} feed_list.append(feed) - return feed_list, False, None, "" def postprocess(self, input_dicts, fetch_data, data_id, log_id): - res_list = [] + rec_list = [] + dt_num = len(self.dt_list) if isinstance(fetch_data, dict): if len(fetch_data) > 0: rec_batch_res = self.ocr_reader.postprocess( fetch_data, with_score=True) for res in rec_batch_res: - res_list.append(res[0]) + rec_list.append(res[0]) elif isinstance(fetch_data, list): for one_batch in fetch_data: one_batch_res = self.ocr_reader.postprocess( one_batch, with_score=True) for res in one_batch_res: - res_list.append(res[0]) - - res = {"res": str(res_list)} + rec_list.append(res[0]) + result_list = [] + for i in range(dt_num): + text = rec_list[i] + dt_box = self.dt_list[i] + result_list.append([text,dt_box.tolist()]) + res = {"result": str(result_list)} return res, None, "" -- GitLab