未验证 提交 e222a96c 编写于 作者: M MissPenguin 提交者: GitHub

Merge pull request #6079 from tink2123/ppocr_v3_doc

add rec introduction, update pdserving
......@@ -39,14 +39,14 @@ python3.7 -m pip install onnxruntime==1.9.0
有两种方式获取Paddle静态图模型:在 [model_list](../../doc/doc_ch/models_list.md) 中下载PaddleOCR提供的预测模型;
参考[模型导出说明](../../doc/doc_ch/inference.md#训练模型转inference模型)把训练好的权重转为 inference_model。
ppocr 中文检测、识别、分类模型为例:
PP-OCRv3 中文检测、识别、分类模型为例:
```
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
cd ./inference && tar xf ch_PP-OCRv2_det_infer.tar && cd ..
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
cd ./inference && tar xf ch_PP-OCRv3_det_infer.tar && cd ..
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar
cd ./inference && tar xf ch_PP-OCRv2_rec_infer.tar && cd ..
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
cd ./inference && tar xf ch_PP-OCRv3_rec_infer.tar && cd ..
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
cd ./inference && tar xf ch_ppocr_mobile_v2.0_cls_infer.tar && cd ..
......@@ -57,7 +57,7 @@ cd ./inference && tar xf ch_ppocr_mobile_v2.0_cls_infer.tar && cd ..
使用 Paddle2ONNX 将Paddle静态图模型转换为ONNX模型格式:
```
paddle2onnx --model_dir ./inference/ch_PP-OCRv2_det_infer \
paddle2onnx --model_dir ./inference/ch_PP-OCRv3_det_infer \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--save_file ./inference/det_onnx/model.onnx \
......@@ -65,7 +65,7 @@ paddle2onnx --model_dir ./inference/ch_PP-OCRv2_det_infer \
--input_shape_dict="{'x':[-1,3,-1,-1]}" \
--enable_onnx_checker True
paddle2onnx --model_dir ./inference/ch_PP-OCRv2_rec_infer \
paddle2onnx --model_dir ./inference/ch_PP-OCRv3_rec_infer \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--save_file ./inference/rec_onnx/model.onnx \
......@@ -105,8 +105,8 @@ python3.7 tools/infer/predict_system.py --use_gpu=False --use_onnx=True \
```
python3.7 tools/infer/predict_system.py --use_gpu=False \
--cls_model_dir=./inference/ch_ppocr_mobile_v2.0_cls_infer \
--rec_model_dir=./inference/ch_PP-OCRv2_rec_infer \
--det_model_dir=./inference/ch_PP-OCRv2_det_infer \
--rec_model_dir=./inference/ch_PP-OCRv3_rec_infer \
--det_model_dir=./inference/ch_PP-OCRv3_det_infer \
--image_dir=./deploy/lite/imgs/lite_demo.png
```
......
......@@ -15,6 +15,14 @@ Some Key Features of Paddle Serving:
- Industrial serving features supported, such as models management, online loading, online A/B testing etc.
- Highly concurrent and efficient communication between clients and servers supported.
PaddleServing supports deployment in multiple languages. In this example, two deployment methods, python pipeline and C++, are provided. The comparison between the two is as follows:
| 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 |
The introduction and tutorial of Paddle Serving service deployment framework reference [document](https://github.com/PaddlePaddle/Serving/blob/develop/README.md).
......@@ -25,6 +33,7 @@ The introduction and tutorial of Paddle Serving service deployment framework ref
- [Environmental preparation](#environmental-preparation)
- [Model conversion](#model-conversion)
- [Paddle Serving pipeline deployment](#paddle-serving-pipeline-deployment)
- [Paddle Serving C++ deployment](#C++)
- [WINDOWS Users](#windows-users)
- [FAQ](#faq)
......@@ -41,23 +50,23 @@ PaddleOCR operating environment and Paddle Serving operating environment are nee
```bash
# Install serving which used to start the service
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.7.0.post102-py3-none-any.whl
pip3 install paddle_serving_server_gpu-0.7.0.post102-py3-none-any.whl
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.8.3.post102-py3-none-any.whl
pip3 install paddle_serving_server_gpu-0.8.3.post102-py3-none-any.whl
# Install paddle-serving-server for cuda10.1
# wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.7.0.post101-py3-none-any.whl
# pip3 install paddle_serving_server_gpu-0.7.0.post101-py3-none-any.whl
# wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.8.3.post101-py3-none-any.whl
# pip3 install paddle_serving_server_gpu-0.8.3.post101-py3-none-any.whl
# Install serving which used to start the service
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.7.0-cp37-none-any.whl
pip3 install paddle_serving_client-0.7.0-cp37-none-any.whl
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.8.3-cp37-none-any.whl
pip3 install paddle_serving_client-0.8.3-cp37-none-any.whl
# Install serving-app
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_app-0.7.0-py3-none-any.whl
pip3 install paddle_serving_app-0.7.0-py3-none-any.whl
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_app-0.8.3-py3-none-any.whl
pip3 install paddle_serving_app-0.8.3-py3-none-any.whl
```
**note:** If you want to install the latest version of PaddleServing, refer to [link](https://github.com/PaddlePaddle/Serving/blob/v0.7.0/doc/Latest_Packages_CN.md).
**note:** If you want to install the latest version of PaddleServing, refer to [link](https://github.com/PaddlePaddle/Serving/blob/v0.8.3/doc/Latest_Packages_CN.md).
<a name="model-conversion"></a>
......@@ -67,37 +76,37 @@ When using PaddleServing for service deployment, you need to convert the saved i
Firstly, download the [inference model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/README_ch.md#pp-ocr%E7%B3%BB%E5%88%97%E6%A8%A1%E5%9E%8B%E5%88%97%E8%A1%A8%E6%9B%B4%E6%96%B0%E4%B8%AD) of PPOCR
```
# Download and unzip the OCR text detection model
wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar -O ch_PP-OCRv2_det_infer.tar && tar -xf ch_PP-OCRv2_det_infer.tar
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar -O ch_PP-OCRv3_det_infer.tar && tar -xf ch_PP-OCRv3_det_infer.tar
# Download and unzip the OCR text recognition model
wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar -O ch_PP-OCRv2_rec_infer.tar && tar -xf ch_PP-OCRv2_rec_infer.tar
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar -O ch_PP-OCRv3_rec_infer.tar && tar -xf ch_PP-OCRv3_rec_infer.tar
```
Then, you can use installed paddle_serving_client tool to convert inference model to mobile model.
```
# Detection model conversion
python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_det_infer/ \
python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv3_det_infer/ \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--serving_server ./ppocr_det_mobile_2.0_serving/ \
--serving_client ./ppocr_det_mobile_2.0_client/
--serving_server ./ppocr_det_v3_serving/ \
--serving_client ./ppocr_det_v3_client/
# Recognition model conversion
python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_rec_infer/ \
python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv3_rec_infer/ \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--serving_server ./ppocr_rec_mobile_2.0_serving/ \
--serving_client ./ppocr_rec_mobile_2.0_client/
--serving_server ./ppocr_rec_v3_serving/ \
--serving_client ./ppocr_rec_v3_client/
```
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:
After the detection model is converted, there will be additional folders of `ppocr_det_v3_serving` and `ppocr_det_v3_client` in the current folder, with the following format:
```
|- ppocr_det_mobile_2.0_serving/
|- ppocr_det_v3_serving/
|- __model__
|- __params__
|- serving_server_conf.prototxt
|- serving_server_conf.stream.prototxt
|- ppocr_det_mobile_2.0_client
|- ppocr_det_v3_client
|- serving_client_conf.prototxt
|- serving_client_conf.stream.prototxt
......@@ -193,16 +202,13 @@ The recognition model is the same.
2021-05-13 03:42:36,979 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
```
<a name="C++"></a>
## 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
......@@ -211,7 +217,7 @@ The C++ service deployment is the same as python in the environment setup and da
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 GeneralInferOp --port 9293 &>log.txt &
python3 -m paddle_serving_server.serve --model ppocr_det_v3_serving ppocr_rec_v3_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)
......@@ -219,7 +225,7 @@ The C++ service deployment is the same as python in the environment setup and da
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:
Change the feed_type field and shape field in ppocr_det_v3_client/serving_client_conf.prototxt to the following:
```
feed_var {
......@@ -234,7 +240,7 @@ The C++ service deployment is the same as python in the environment setup and da
start the client:
```
python3 ocr_cpp_client.py ppocrv2_det_client ppocrv2_rec_client
python3 ocr_cpp_client.py ppocr_det_v3_client ppocr_rec_v3_client
```
After successfully running, the predicted result of the model will be printed in the cmd window. An example of the result is:
![](./imgs/results.png)
......
......@@ -9,13 +9,21 @@ PaddleOCR提供2种服务部署方式:
# 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。
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。
相比较于hubserving部署,PaddleServing具备以下优点:
- 支持客户端和服务端之间高并发和高效通信
- 支持 工业级的服务能力 例如模型管理,在线加载,在线A/B测试等
- 支持 多种编程语言 开发客户端,例如C++, Python和Java
PaddleServing 支持多种语言部署,本例中提供了python pipeline 和 C++ 两种部署方式,两者的对比如下:
| 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|
更多有关PaddleServing服务化部署框架介绍和使用教程参考[文档](https://github.com/PaddlePaddle/Serving/blob/develop/README_CN.md)
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)
......@@ -24,6 +32,7 @@ AIStudio演示案例可参考 [基于PaddleServing的OCR服务化部署实战](h
- [环境准备](#环境准备)
- [模型转换](#模型转换)
- [Paddle Serving pipeline部署](#部署)
- [Paddle Serving C++部署](#C++)
- [Windows用户](#Windows用户)
- [FAQ](#FAQ)
......@@ -34,26 +43,33 @@ AIStudio演示案例可参考 [基于PaddleServing的OCR服务化部署实战](h
- 准备PaddleOCR的运行环境[链接](../../doc/doc_ch/installation.md)
```
git clone https://github.com/PaddlePaddle/PaddleOCR
# 进入到工作目录
cd PaddleOCR/deploy/pdserving/
```
- 准备PaddleServing的运行环境,步骤如下
```bash
# 安装serving,用于启动服务
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.7.0.post102-py3-none-any.whl
pip3 install paddle_serving_server_gpu-0.7.0.post102-py3-none-any.whl
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.8.3.post102-py3-none-any.whl
pip3 install paddle_serving_server_gpu-0.8.3.post102-py3-none-any.whl
# 如果是cuda10.1环境,可以使用下面的命令安装paddle-serving-server
# wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.7.0.post101-py3-none-any.whl
# pip3 install paddle_serving_server_gpu-0.7.0.post101-py3-none-any.whl
# wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.8.3.post101-py3-none-any.whl
# pip3 install paddle_serving_server_gpu-0.8.3.post101-py3-none-any.whl
# 安装client,用于向服务发送请求
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.7.0-cp37-none-any.whl
pip3 install paddle_serving_client-0.7.0-cp37-none-any.whl
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.8.3-cp37-none-any.whl
pip3 install paddle_serving_client-0.8.3-cp37-none-any.whl
# 安装serving-app
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_app-0.7.0-py3-none-any.whl
pip3 install paddle_serving_app-0.7.0-py3-none-any.whl
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_app-0.8.3-py3-none-any.whl
pip3 install paddle_serving_app-0.8.3-py3-none-any.whl
```
**Note:** 如果要安装最新版本的PaddleServing参考[链接](https://github.com/PaddlePaddle/Serving/blob/v0.7.0/doc/Latest_Packages_CN.md)
**Note:** 如果要安装最新版本的PaddleServing参考[链接](https://github.com/PaddlePaddle/Serving/blob/v0.8.3/doc/Latest_Packages_CN.md)
<a name="模型转换"></a>
## 模型转换
......@@ -64,38 +80,38 @@ pip3 install paddle_serving_app-0.7.0-py3-none-any.whl
```bash
# 下载并解压 OCR 文本检测模型
wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar -O ch_PP-OCRv2_det_infer.tar && tar -xf ch_PP-OCRv2_det_infer.tar
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar -O ch_PP-OCRv3_det_infer.tar && tar -xf ch_PP-OCRv3_det_infer.tar
# 下载并解压 OCR 文本识别模型
wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar -O ch_PP-OCRv2_rec_infer.tar && tar -xf ch_PP-OCRv2_rec_infer.tar
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar -O ch_PP-OCRv3_rec_infer.tar && tar -xf ch_PP-OCRv3_rec_infer.tar
```
接下来,用安装的paddle_serving_client把下载的inference模型转换成易于server部署的模型格式。
```bash
# 转换检测模型
python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_det_infer/ \
python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv3_det_infer/ \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--serving_server ./ppocr_det_mobile_2.0_serving/ \
--serving_client ./ppocr_det_mobile_2.0_client/
--serving_server ./ppocr_det_v3_serving/ \
--serving_client ./ppocr_det_v3_client/
# 转换识别模型
python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_rec_infer/ \
python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv3_rec_infer/ \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--serving_server ./ppocr_rec_mobile_2.0_serving/ \
--serving_client ./ppocr_rec_mobile_2.0_client/
--serving_server ./ppocr_rec_v3_serving/ \
--serving_client ./ppocr_rec_v3_client/
```
检测模型转换完成后,会在当前文件夹多出`ppocr_det_mobile_2.0_serving``ppocr_det_mobile_2.0_client`的文件夹,具备如下格式:
检测模型转换完成后,会在当前文件夹多出`ppocr_det_v3_serving``ppocr_det_v3_client`的文件夹,具备如下格式:
```
|- ppocr_det_mobile_2.0_serving/
|- ppocr_det_v3_serving/
|- __model__
|- __params__
|- serving_server_conf.prototxt
|- serving_server_conf.stream.prototxt
|- ppocr_det_mobile_2.0_client
|- ppocr_det_v3_client
|- serving_client_conf.prototxt
|- serving_client_conf.stream.prototxt
......@@ -105,13 +121,8 @@ python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_rec_infer/ \
<a name="部署"></a>
## Paddle Serving pipeline部署
1. 下载PaddleOCR代码,若已下载可跳过此步骤
```
git clone https://github.com/PaddlePaddle/PaddleOCR
1. 确认工作目录下文件结构:
# 进入到工作目录
cd PaddleOCR/deploy/pdserving/
```
pdserver目录包含启动pipeline服务和发送预测请求的代码,包括:
```
__init__.py
......@@ -196,16 +207,12 @@ 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。
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
......@@ -223,7 +230,7 @@ cp -rf general_detection_op.cpp Serving/core/general-server/op
```
# 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 GeneralInferOp --port 9293 &>log.txt &
python3 -m paddle_serving_server.serve --model ppocr_det_v3_serving ppocr_rec_v3_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)
......@@ -231,7 +238,7 @@ cp -rf general_detection_op.cpp Serving/core/general-server/op
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:
ppocr_det_v3_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"
......@@ -243,7 +250,7 @@ cp -rf general_detection_op.cpp Serving/core/general-server/op
```
启动客户端
```
python3 ocr_cpp_client.py ppocrv2_det_client ppocrv2_rec_client
python3 ocr_cpp_client.py ppocr_det_v3_client ppocr_rec_v3_client
```
成功运行后,模型预测的结果会打印在cmd窗口中,结果示例为:
......
......@@ -34,7 +34,7 @@ op:
client_type: local_predictor
#det模型路径
model_config: ./ppocr_det_mobile_2.0_serving
model_config: ./ppocr_det_v3_serving
#Fetch结果列表,以client_config中fetch_var的alias_name为准
fetch_list: ["save_infer_model/scale_0.tmp_1"]
......@@ -60,10 +60,10 @@ op:
client_type: local_predictor
#rec模型路径
model_config: ./ppocr_rec_mobile_2.0_serving
model_config: ./ppocr_rec_v3_serving
#Fetch结果列表,以client_config中fetch_var的alias_name为准
fetch_list: ["save_infer_model/scale_0.tmp_1"]
fetch_list: ["softmax_5.tmp_0"]
#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"
......
......@@ -392,38 +392,8 @@ class OCRReader(object):
return norm_img_batch[0]
def postprocess_old(self, outputs, with_score=False):
rec_res = []
rec_idx_lod = outputs["ctc_greedy_decoder_0.tmp_0.lod"]
rec_idx_batch = outputs["ctc_greedy_decoder_0.tmp_0"]
if with_score:
predict_lod = outputs["softmax_0.tmp_0.lod"]
for rno in range(len(rec_idx_lod) - 1):
beg = rec_idx_lod[rno]
end = rec_idx_lod[rno + 1]
if isinstance(rec_idx_batch, list):
rec_idx_tmp = [x[0] for x in rec_idx_batch[beg:end]]
else: #nd array
rec_idx_tmp = rec_idx_batch[beg:end, 0]
preds_text = self.char_ops.decode(rec_idx_tmp)
if with_score:
beg = predict_lod[rno]
end = predict_lod[rno + 1]
if isinstance(outputs["softmax_0.tmp_0"], list):
outputs["softmax_0.tmp_0"] = np.array(outputs[
"softmax_0.tmp_0"]).astype(np.float32)
probs = outputs["softmax_0.tmp_0"][beg:end, :]
ind = np.argmax(probs, axis=1)
blank = probs.shape[1]
valid_ind = np.where(ind != (blank - 1))[0]
score = np.mean(probs[valid_ind, ind[valid_ind]])
rec_res.append([preds_text, score])
else:
rec_res.append([preds_text])
return rec_res
def postprocess(self, outputs, with_score=False):
preds = outputs["save_infer_model/scale_0.tmp_1"]
preds = outputs["softmax_5.tmp_0"]
try:
preds = preds.numpy()
except:
......
......@@ -392,38 +392,8 @@ class OCRReader(object):
return norm_img_batch[0]
def postprocess_old(self, outputs, with_score=False):
rec_res = []
rec_idx_lod = outputs["ctc_greedy_decoder_0.tmp_0.lod"]
rec_idx_batch = outputs["ctc_greedy_decoder_0.tmp_0"]
if with_score:
predict_lod = outputs["softmax_0.tmp_0.lod"]
for rno in range(len(rec_idx_lod) - 1):
beg = rec_idx_lod[rno]
end = rec_idx_lod[rno + 1]
if isinstance(rec_idx_batch, list):
rec_idx_tmp = [x[0] for x in rec_idx_batch[beg:end]]
else: #nd array
rec_idx_tmp = rec_idx_batch[beg:end, 0]
preds_text = self.char_ops.decode(rec_idx_tmp)
if with_score:
beg = predict_lod[rno]
end = predict_lod[rno + 1]
if isinstance(outputs["softmax_0.tmp_0"], list):
outputs["softmax_0.tmp_0"] = np.array(outputs[
"softmax_0.tmp_0"]).astype(np.float32)
probs = outputs["softmax_0.tmp_0"][beg:end, :]
ind = np.argmax(probs, axis=1)
blank = probs.shape[1]
valid_ind = np.where(ind != (blank - 1))[0]
score = np.mean(probs[valid_ind, ind[valid_ind]])
rec_res.append([preds_text, score])
else:
rec_res.append([preds_text])
return rec_res
def postprocess(self, outputs, with_score=False):
preds = outputs["save_infer_model/scale_0.tmp_1"]
preds = outputs["softmax_5.tmp_0"]
try:
preds = preds.numpy()
except:
......
# CRNN
- [1. 算法简介](#1)
- [2. 环境配置](#2)
- [3. 模型训练、评估、预测](#3)
- [3.1 训练](#3-1)
- [3.2 评估](#3-2)
- [3.3 预测](#3-3)
- [4. 推理部署](#4)
- [4.1 Python推理](#4-1)
- [4.2 C++推理](#4-2)
- [4.3 Serving服务化部署](#4-3)
- [4.4 更多推理部署](#4-4)
- [5. FAQ](#5)
<a name="1"></a>
## 1. 算法简介
论文信息:
> [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://arxiv.org/abs/1507.05717)
> Baoguang Shi, Xiang Bai, Cong Yao
> IEEE, 2015
参考[DTRB](https://arxiv.org/abs/1904.01906) 文字识别训练和评估流程,使用MJSynth和SynthText两个文字识别数据集训练,在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估,算法效果如下:
|模型|骨干网络|Avg Accuracy|配置文件|下载链接|
|---|---|---|---|---|
|CRNN|Resnet34_vd|81.04%|[configs/rec/rec_r34_vd_none_bilstm_ctc.yml](../../configs/rec/rec_r34_vd_none_bilstm_ctc.yml)|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar)|
|CRNN|MobileNetV3|77.95%|[configs/rec/rec_mv3_none_bilstm_ctc.yml](../../configs/rec/rec_mv3_none_bilstm_ctc.yml)|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar)|
<a name="2"></a>
## 2. 环境配置
请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境,参考[《项目克隆》](./clone.md)克隆项目代码。
<a name="3"></a>
## 3. 模型训练、评估、预测
请参考[文本识别训练教程](./recognition.md)。PaddleOCR对代码进行了模块化,训练不同的识别模型只需要**更换配置文件**即可。
- 训练
在完成数据准备后,便可以启动训练,训练命令如下:
```
#单卡训练(训练周期长,不建议)
python3 tools/train.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml
#多卡训练,通过--gpus参数指定卡号
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c rec_r34_vd_none_bilstm_ctc.yml
```
- 评估
```
# GPU 评估, Global.pretrained_model 为待测权重
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
```
- 预测:
```
# 预测使用的配置文件必须与训练一致
python3 tools/infer_rec.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
```
<a name="4"></a>
## 4. 推理部署
<a name="4-1"></a>
### 4.1 Python推理
首先将 CRNN 文本识别训练过程中保存的模型,转换成inference model。以基于Resnet34_vd骨干网络,使用MJSynth和SynthText两个英文文本识别合成数据集训练的[模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar) 为例,可以使用如下命令进行转换:
```shell
python3 tools/export_model.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_crnn
```
CRNN 文本识别模型推理,可以执行如下命令:
```shell
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rec_crnn/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
```
![](../imgs_words_en/word_336.png)
执行命令后,上面图像的识别结果如下:
```bash
Predicts of ./doc/imgs_words_en/word_336.png:('super', 0.9999073)
```
**注意**:由于上述模型是参考[DTRB](https://arxiv.org/abs/1904.01906)文本识别训练和评估流程,与超轻量级中文识别模型训练有两方面不同:
- 训练时采用的图像分辨率不同,训练上述模型采用的图像分辨率是[3,32,100],而中文模型训练时,为了保证长文本的识别效果,训练时采用的图像分辨率是[3, 32, 320]。预测推理程序默认的的形状参数是训练中文采用的图像分辨率,即[3, 32, 320]。因此,这里推理上述英文模型时,需要通过参数rec_image_shape设置识别图像的形状。
- 字符列表,DTRB论文中实验只是针对26个小写英文本母和10个数字进行实验,总共36个字符。所有大小字符都转成了小写字符,不在上面列表的字符都忽略,认为是空格。因此这里没有输入字符字典,而是通过如下命令生成字典.因此在推理时需要设置参数rec_char_dict_path,指定为英文字典"./ppocr/utils/ic15_dict.txt"。
```
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
dict_character = list(self.character_str)
```
<a name="4-2"></a>
### 4.2 C++推理
准备好推理模型后,参考[cpp infer](../../deploy/cpp_infer/)教程进行操作即可。
<a name="4-3"></a>
### 4.3 Serving服务化部署
准备好推理模型后,参考[pdserving](../../deploy/pdserving/)教程进行Serving服务化部署,包括Python Serving和C++ Serving两种模式。
<a name="4-4"></a>
### 4.4 更多推理部署
CRNN模型还支持以下推理部署方式:
- Paddle2ONNX推理:准备好推理模型后,参考[paddle2onnx](../../deploy/paddle2onnx/)教程操作。
<a name="5"></a>
## 5. FAQ
## 引用
```bibtex
@ARTICLE{7801919,
author={Shi, Baoguang and Bai, Xiang and Yao, Cong},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition},
year={2017},
volume={39},
number={11},
pages={2298-2304},
doi={10.1109/TPAMI.2016.2646371}}
```
# SEED
- [1. 算法简介](#1)
- [2. 环境配置](#2)
- [3. 模型训练、评估、预测](#3)
- [3.1 训练](#3-1)
- [3.2 评估](#3-2)
- [3.3 预测](#3-3)
- [4. 推理部署](#4)
- [4.1 Python推理](#4-1)
- [4.2 C++推理](#4-2)
- [4.3 Serving服务化部署](#4-3)
- [4.4 更多推理部署](#4-4)
- [5. FAQ](#5)
<a name="1"></a>
## 1. 算法简介
论文信息:
> [SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition](https://arxiv.org/pdf/2005.10977.pdf)
> Qiao, Zhi and Zhou, Yu and Yang, Dongbao and Zhou, Yucan and Wang, Weiping
> CVPR, 2020
参考[DTRB](https://arxiv.org/abs/1904.01906) 文字识别训练和评估流程,使用MJSynth和SynthText两个文字识别数据集训练,在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估,算法效果如下:
|模型|骨干网络|Avg Accuracy|配置文件|下载链接|
|---|---|---|---|---|
|SEED|Aster_Resnet| 85.2% | [configs/rec/rec_resnet_stn_bilstm_att.yml](../../configs/rec/rec_resnet_stn_bilstm_att.yml) | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) |
<a name="2"></a>
## 2. 环境配置
请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境,参考[《项目克隆》](./clone.md)克隆项目代码。
<a name="3"></a>
## 3. 模型训练、评估、预测
请参考[文本识别训练教程](./recognition.md)。PaddleOCR对代码进行了模块化,训练不同的识别模型只需要**更换配置文件**即可。
- 训练
SEED模型需要额外加载FastText训练好的[语言模型](https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.en.300.bin.gz) ,并且安装 fasttext 依赖:
```
python3 -m pip install fasttext==0.9.1
```
然后,在完成数据准备后,便可以启动训练,训练命令如下:
```
#单卡训练(训练周期长,不建议)
python3 tools/train.py -c configs/rec/rec_resnet_stn_bilstm_att.yml
#多卡训练,通过--gpus参数指定卡号
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c rec_resnet_stn_bilstm_att.yml
```
- 评估
```
# GPU 评估, Global.pretrained_model 为待测权重
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_resnet_stn_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
```
- 预测:
```
# 预测使用的配置文件必须与训练一致
python3 tools/infer_rec.py -c configs/rec/rec_resnet_stn_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
```
<a name="4"></a>
## 4. 推理部署
<a name="4-1"></a>
### 4.1 Python推理
comming soon
<a name="4-2"></a>
### 4.2 C++推理
comming soon
<a name="4-3"></a>
### 4.3 Serving服务化部署
comming soon
<a name="4-4"></a>
### 4.4 更多推理部署
comming soon
<a name="5"></a>
## 5. FAQ
## 引用
```bibtex
@inproceedings{qiao2020seed,
title={Seed: Semantics enhanced encoder-decoder framework for scene text recognition},
author={Qiao, Zhi and Zhou, Yu and Yang, Dongbao and Zhou, Yucan and Wang, Weiping},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={13528--13537},
year={2020}
}
```
# STAR-Net
- [1. 算法简介](#1)
- [2. 环境配置](#2)
- [3. 模型训练、评估、预测](#3)
- [3.1 训练](#3-1)
- [3.2 评估](#3-2)
- [3.3 预测](#3-3)
- [4. 推理部署](#4)
- [4.1 Python推理](#4-1)
- [4.2 C++推理](#4-2)
- [4.3 Serving服务化部署](#4-3)
- [4.4 更多推理部署](#4-4)
- [5. FAQ](#5)
<a name="1"></a>
## 1. 算法简介
论文信息:
> [STAR-Net: a spatial attention residue network for scene text recognition.](http://www.bmva.org/bmvc/2016/papers/paper043/paper043.pdf)
> Wei Liu, Chaofeng Chen, Kwan-Yee K. Wong, Zhizhong Su and Junyu Han.
> BMVC, pages 43.1-43.13, 2016
参考[DTRB](https://arxiv.org/abs/1904.01906) 文字识别训练和评估流程,使用MJSynth和SynthText两个文字识别数据集训练,在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估,算法效果如下:
|模型|骨干网络|Avg Accuracy|配置文件|下载链接|
|---|---|---|---|---|
|StarNet|Resnet34_vd|84.44%|[configs/rec/rec_r34_vd_tps_bilstm_ctc.yml](../../configs/rec/rec_r34_vd_tps_bilstm_ctc.yml)|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar)|
|StarNet|MobileNetV3|81.42%|[configs/rec/rec_mv3_tps_bilstm_ctc.yml](../../configs/rec/rec_mv3_tps_bilstm_ctc.yml)|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_ctc_v2.0_train.tar)|
<a name="2"></a>
## 2. 环境配置
请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境,参考[《项目克隆》](./clone.md)克隆项目代码。
<a name="3"></a>
## 3. 模型训练、评估、预测
请参考[文本识别训练教程](./recognition.md)。PaddleOCR对代码进行了模块化,训练不同的识别模型只需要**更换配置文件**即可。
- 训练
在完成数据准备后,便可以启动训练,训练命令如下:
```
#单卡训练(训练周期长,不建议)
python3 tools/train.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml
#多卡训练,通过--gpus参数指定卡号
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c rec_r34_vd_tps_bilstm_ctc.yml
```
- 评估
```
# GPU 评估, Global.pretrained_model 为待测权重
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
```
- 预测:
```
# 预测使用的配置文件必须与训练一致
python3 tools/infer_rec.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
```
<a name="4"></a>
## 4. 推理部署
<a name="4-1"></a>
### 4.1 Python推理
首先将 STAR-Net 文本识别训练过程中保存的模型,转换成inference model。以基于Resnet34_vd骨干网络,使用MJSynth和SynthText两个英文文本识别合成数据集训练的[模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar) 为例,可以使用如下命令进行转换:
```shell
python3 tools/export_model.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_tps_bilstm_ctc_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_starnet
```
STAR-Net 文本识别模型推理,可以执行如下命令:
```shell
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rec_starnet/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
```
![](../imgs_words_en/word_336.png)
执行命令后,上面图像的识别结果如下:
```bash
Predicts of ./doc/imgs_words_en/word_336.png:('super', 0.9999073)
```
**注意**:由于上述模型是参考[DTRB](https://arxiv.org/abs/1904.01906)文本识别训练和评估流程,与超轻量级中文识别模型训练有两方面不同:
- 训练时采用的图像分辨率不同,训练上述模型采用的图像分辨率是[3,32,100],而中文模型训练时,为了保证长文本的识别效果,训练时采用的图像分辨率是[3, 32, 320]。预测推理程序默认的的形状参数是训练中文采用的图像分辨率,即[3, 32, 320]。因此,这里推理上述英文模型时,需要通过参数rec_image_shape设置识别图像的形状。
- 字符列表,DTRB论文中实验只是针对26个小写英文本母和10个数字进行实验,总共36个字符。所有大小字符都转成了小写字符,不在上面列表的字符都忽略,认为是空格。因此这里没有输入字符字典,而是通过如下命令生成字典.因此在推理时需要设置参数rec_char_dict_path,指定为英文字典"./ppocr/utils/ic15_dict.txt"。
```
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
dict_character = list(self.character_str)
```
<a name="4-2"></a>
### 4.2 C++推理
准备好推理模型后,参考[cpp infer](../../deploy/cpp_infer/)教程进行操作即可。
<a name="4-3"></a>
### 4.3 Serving服务化部署
准备好推理模型后,参考[pdserving](../../deploy/pdserving/)教程进行Serving服务化部署,包括Python Serving和C++ Serving两种模式。
<a name="4-4"></a>
### 4.4 更多推理部署
STAR-Net模型还支持以下推理部署方式:
- Paddle2ONNX推理:准备好推理模型后,参考[paddle2onnx](../../deploy/paddle2onnx/)教程操作。
<a name="5"></a>
## 5. FAQ
## 引用
```bibtex
@inproceedings{liu2016star,
title={STAR-Net: a spatial attention residue network for scene text recognition.},
author={Liu, Wei and Chen, Chaofeng and Wong, Kwan-Yee K and Su, Zhizhong and Han, Junyu},
booktitle={BMVC},
volume={2},
pages={7},
year={2016}
}
```
......@@ -52,6 +52,27 @@ PP-OCRv3文本检测从网络结构、蒸馏训练策略两个方向做了进一
|4|1 + 2 + LKPAN|4.6M|86.0|156ms|
PP-OCRv3识别从网络结构、训练策略、数据增强三个方向做了进一步优化:
- 网络结构上:使用[SVTR](todo:add_link)中的 Transformer block 替换LSTM,提升模型精度和预测速度;
- 训练策略上:参考 [GTC](https://arxiv.org/pdf/2002.01276.pdf) 策略,使用注意力机制模块指导CTC训练,定位和识别字符,提升不规则文本的识别精度;设计方向分类前序任务,获取更优预训练模型,加速模型收敛过程,提升精度。
- 数据增强上:使用[RecConAug](todo:add_link)数据增广方法,随机结合图片,提升训练数据的上下文信息丰富度,增强模型鲁棒性。
基于上述策略,PP-OCRv3识别模型相比上一版本,速度加速30%,精度进一步提升4.5%。 具体消融实验:
| id | 策略 | 模型大小 | 精度 | CPU+mkldnn 预测耗时 |
|-----|-----|--------|----|------------|
| 01 | PP-OCRv2 | 8M | 69.3% | 26ms |
| 02 | SVTR_tiny | 19M | 80.1% | - |
| 03 | LCNet_SVTR_G6 | 8.2M | 76% | - |
| 04 | LCNet_SVTR_G1 | - | - | - |
| 05 | PP-OCRv3 | 12M | 71.9% | 19ms |
| 06 | + GTC | 12M | 75.8% | 19ms |
| 07 | + RecConAug | 12M | 76.3% | 19ms |
| 08 | + SSL pretrain | 12M | 76.9% | 19ms |
| 09 | + UDML | 12M | 78.4% | 19ms |
| 10 | + unlabeled data | 12M | 79.4% | 19ms |
<a name="2"></a>
## 2. 特性
......
......@@ -105,8 +105,6 @@ train_data/rec/train/word_002.jpg 用科技让复杂的世界更简单
若您本地没有数据集,可以在官网下载 [ICDAR2015](http://rrc.cvc.uab.es/?ch=4&com=downloads) 数据,用于快速验证。也可以参考[DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here) ,下载 benchmark 所需的lmdb格式数据集。
如果希望复现SAR的论文指标,需要下载[SynthAdd](https://pan.baidu.com/share/init?surl=uV0LtoNmcxbO-0YA7Ch4dg), 提取码:627x。此外,真实数据集icdar2013, icdar2015, cocotext, IIIT5也作为训练数据的一部分。具体数据细节可以参考论文SAR。
如果你使用的是icdar2015的公开数据集,PaddleOCR 提供了一份用于训练 ICDAR2015 数据集的标签文件,通过以下方式下载:
```
......@@ -169,12 +167,13 @@ PaddleOCR内置了一部分字典,可以按需使用。
`ppocr/utils/en_dict.txt` 是一个包含96个字符的英文字典
目前的多语言模型仍处在demo阶段,会持续优化模型并补充语种,**非常欢迎您为我们提供其他语言的字典和字体**
如您愿意可将字典文件提交至 [dict](../../ppocr/utils/dict),我们会在Repo中感谢您。
- 自定义字典
如需自定义dic文件,请在 `configs/rec/rec_icdar15_train.yml` 中添加 `character_dict_path` 字段, 指向您的字典路径。
如需自定义dic文件,请在 `configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml` 中添加 `character_dict_path` 字段, 指向您的字典路径。
<a name="支持空格"></a>
## 1.4 添加空格类别
......@@ -195,7 +194,7 @@ PaddleOCR提供了多种数据增强方式,默认配置文件中已经添加
<a name="开始训练"></a>
# 2. 开始训练
PaddleOCR提供了训练脚本、评估脚本和预测脚本,本节将以 CRNN 识别模型为例:
PaddleOCR提供了训练脚本、评估脚本和预测脚本,本节将以 PP-OCRv3 英文识别模型为例:
<a name="启动训练"></a>
## 2.1 启动训练
......@@ -204,11 +203,11 @@ PaddleOCR提供了训练脚本、评估脚本和预测脚本,本节将以 CRNN
```
cd PaddleOCR/
# 下载MobileNetV3的预训练模型
wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar
# 下载英文PP-OCRv3的预训练模型
wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_train.tar
# 解压模型参数
cd pretrain_models
tar -xf rec_mv3_none_bilstm_ctc_v2.0_train.tar && rm -rf rec_mv3_none_bilstm_ctc_v2.0_train.tar
tar -xf en_PP-OCRv3_rec_train.tar && rm -rf en_PP-OCRv3_rec_train.tar
```
开始训练:
......@@ -220,44 +219,23 @@ tar -xf rec_mv3_none_bilstm_ctc_v2.0_train.tar && rm -rf rec_mv3_none_bilstm_ctc
# 训练icdar15英文数据 训练日志会自动保存为 "{save_model_dir}" 下的train.log
#单卡训练(训练周期长,不建议)
python3 tools/train.py -c configs/rec/rec_icdar15_train.yml
python3 tools/train.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model=./pretrain_models/en_PP-OCRv3_rec_train/best_accuracy
#多卡训练,通过--gpus参数指定卡号
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_icdar15_train.yml
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model=./pretrain_models/en_PP-OCRv3_rec_train/best_accuracy
```
PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_train.yml` 中修改 `eval_batch_step` 设置评估频率,默认每500个iter评估一次。评估过程中默认将最佳acc模型,保存为 `output/rec_CRNN/best_accuracy`
PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml` 中修改 `eval_batch_step` 设置评估频率,默认每500个iter评估一次。评估过程中默认将最佳acc模型,保存为 `output/en_PP-OCRv3_rec/best_accuracy`
如果验证集很大,测试将会比较耗时,建议减少评估次数,或训练完再进行评估。
**提示:** 可通过 -c 参数选择 `configs/rec/` 路径下的多种模型配置进行训练,PaddleOCR支持的识别算法
**提示:** 可通过 -c 参数选择 `configs/rec/` 路径下的多种模型配置进行训练,PaddleOCR支持的识别算法可以参考[前沿算法列表](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/algorithm_overview.md#12-%E6%96%87%E6%9C%AC%E8%AF%86%E5%88%AB%E7%AE%97%E6%B3%95)
| 配置文件 | 算法名称 | backbone | trans | seq | pred |
| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: |
| [rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml) | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc |
| [rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml) | CRNN | ResNet34_vd | None | BiLSTM | ctc |
| rec_icdar15_train.yml | CRNN | Mobilenet_v3 large 0.5 | None | BiLSTM | ctc |
| rec_mv3_none_bilstm_ctc.yml | CRNN | Mobilenet_v3 large 0.5 | None | BiLSTM | ctc |
| rec_mv3_none_none_ctc.yml | Rosetta | Mobilenet_v3 large 0.5 | None | None | ctc |
| rec_r34_vd_none_bilstm_ctc.yml | CRNN | Resnet34_vd | None | BiLSTM | ctc |
| rec_r34_vd_none_none_ctc.yml | Rosetta | Resnet34_vd | None | None | ctc |
| rec_mv3_tps_bilstm_att.yml | CRNN | Mobilenet_v3 | TPS | BiLSTM | att |
| rec_r34_vd_tps_bilstm_att.yml | CRNN | Resnet34_vd | TPS | BiLSTM | att |
| rec_r50fpn_vd_none_srn.yml | SRN | Resnet50_fpn_vd | None | rnn | srn |
| rec_mtb_nrtr.yml | NRTR | nrtr_mtb | None | transformer encoder | transformer decoder |
| rec_r31_sar.yml | SAR | ResNet31 | None | LSTM encoder | LSTM decoder |
| rec_resnet_stn_bilstm_att.yml | SEED | Aster_Resnet | STN | BiLSTM | att |
训练中文数据,推荐使用[ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml),如您希望尝试其他算法在中文数据集上的效果,请参考下列说明修改配置文件:
*其中SEED模型需要额外加载FastText训练好的[语言模型](https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.en.300.bin.gz) ,并且安装 fasttext 依赖:
```
python3.7 -m pip install fasttext==0.9.1
```
训练中文数据,推荐使用[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml),如您希望尝试其他算法在中文数据集上的效果,请参考下列说明修改配置文件:
`rec_chinese_lite_train_v2.0.yml` 为例:
`ch_PP-OCRv3_rec_distillation.yml` 为例:
```
Global:
...
......@@ -290,7 +268,7 @@ Train:
...
- RecResizeImg:
# 修改 image_shape 以适应长文本
image_shape: [3, 32, 320]
image_shape: [3, 48, 320]
...
loader:
...
......@@ -310,7 +288,7 @@ Eval:
...
- RecResizeImg:
# 修改 image_shape 以适应长文本
image_shape: [3, 32, 320]
image_shape: [3, 48, 320]
...
loader:
# 单卡验证的batch_size
......@@ -325,7 +303,7 @@ Eval:
如果训练程序中断,如果希望加载训练中断的模型从而恢复训练,可以通过指定Global.checkpoints指定要加载的模型路径:
```shell
python3 tools/train.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints=./your/trained/model
python3 tools/train.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.checkpoints=./your/trained/model
```
**注意**`Global.checkpoints`的优先级高于`Global.pretrained_model`的优先级,即同时指定两个参数时,优先加载`Global.checkpoints`指定的模型,如果`Global.checkpoints`指定的模型路径有误,会加载`Global.pretrained_model`指定的模型。
......@@ -383,8 +361,8 @@ args1: args1
如果您想进一步加快训练速度,可以使用[自动混合精度训练](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/amp_cn.html), 以单机单卡为例,命令如下:
```shell
python3 tools/train.py -c configs/rec/rec_icdar15_train.yml \
-o Global.pretrained_model=./pretrain_models/rec_mv3_none_bilstm_ctc_v2.0_train \
python3 tools/train.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml \
-o Global.pretrained_model=./pretrain_models/en_PP-OCRv3_rec_train/best_accuracy \
Global.use_amp=True Global.scale_loss=1024.0 Global.use_dynamic_loss_scaling=True
```
......@@ -394,8 +372,8 @@ python3 tools/train.py -c configs/rec/rec_icdar15_train.yml \
多机多卡训练时,通过 `--ips` 参数设置使用的机器IP地址,通过 `--gpus` 参数设置使用的GPU ID:
```bash
python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_icdar15_train.yml \
-o Global.pretrained_model=./pretrain_models/rec_mv3_none_bilstm_ctc_v2.0_train
python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1,2,3' tools/train.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml \
-o Global.pretrained_model=./pretrain_models/en_PP-OCRv3_rec_train/best_accuracy
```
**注意:** 采用多机多卡训练时,需要替换上面命令中的ips值为您机器的地址,机器之间需要能够相互ping通。另外,训练时需要在多个机器上分别启动命令。查看机器ip地址的命令为`ifconfig`。
......@@ -485,11 +463,12 @@ DCU设备上运行需要设置环境变量 `export HIP_VISIBLE_DEVICES=0,1,2,3`
<a name="31-----"></a>
## 3.1 指标评估
训练中模型参数默认保存在`Global.save_model_dir`目录下。在评估指标时,需要设置`Global.checkpoints`指向保存的参数文件。评估数据集可以通过 `configs/rec/rec_icdar15_train.yml` 修改Eval中的 `label_file_path` 设置。
训练中模型参数默认保存在`Global.save_model_dir`目录下。在评估指标时,需要设置`Global.checkpoints`指向保存的参数文件。评估数据集可以通过 `configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml` 修改Eval中的 `label_file_path` 设置。
```
# GPU 评估, Global.checkpoints 为待测权重
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.checkpoints={path/to/weights}/best_accuracy
```
<a name="32-------"></a>
......@@ -519,7 +498,7 @@ output/rec/
```
# 预测英文结果
python3 tools/infer_rec.py -c configs/rec/rec_icdar15_train.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/en/word_1.png
python3 tools/infer_rec.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
```
预测图片:
......@@ -538,7 +517,7 @@ infer_img: doc/imgs_words/en/word_1.png
```
# 预测中文结果
python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/ch/word_1.jpg
python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/ch/word_1.jpg
```
预测图片:
......@@ -569,15 +548,15 @@ inference 模型(`paddle.jit.save`保存的模型)
# Global.pretrained_model 参数设置待转换的训练模型地址,不用添加文件后缀 .pdmodel,.pdopt或.pdparams。
# Global.save_inference_dir参数设置转换的模型将保存的地址。
python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.save_inference_dir=./inference/rec_crnn/
python3 tools/export_model.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model=./pretrain_models/en_PP-OCRv3_rec_train/best_accuracy Global.save_inference_dir=./inference/en_PP-OCRv3_rec/
```
**注意:**如果您是在自己的数据集上训练的模型,并且调整了中文字符的字典文件,请注意修改配置文件中的`character_dict_path`是否是所需要的字典文件。
**注意:**如果您是在自己的数据集上训练的模型,并且调整了中文字符的字典文件,请注意修改配置文件中的`character_dict_path`为自定义字典文件。
转换成功后,在目录下有三个文件:
```
/inference/rec_crnn/
inference/en_PP-OCRv3_rec/
├── inference.pdiparams # 识别inference模型的参数文件
├── inference.pdiparams.info # 识别inference模型的参数信息,可忽略
└── inference.pdmodel # 识别inference模型的program文件
......@@ -588,7 +567,7 @@ python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_trai
如果训练时修改了文本的字典,在使用inference模型预测时,需要通过`--rec_char_dict_path`指定使用的字典路径
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_dict_path="your text dict path"
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 48, 320" --rec_char_dict_path="your text dict path"
```
......
# STAR-Net
- [1. Introduction](#1)
- [2. Environment](#2)
- [3. Model Training / Evaluation / Prediction](#3)
- [3.1 Training](#3-1)
- [3.2 Evaluation](#3-2)
- [3.3 Prediction](#3-3)
- [4. Inference and Deployment](#4)
- [4.1 Python Inference](#4-1)
- [4.2 C++ Inference](#4-2)
- [4.3 Serving](#4-3)
- [4.4 More](#4-4)
- [5. FAQ](#5)
<a name="1"></a>
## 1. Introduction
Paper:
> [STAR-Net: a spatial attention residue network for scene text recognition.](http://www.bmva.org/bmvc/2016/papers/paper043/paper043.pdf)
> Wei Liu, Chaofeng Chen, Kwan-Yee K. Wong, Zhizhong Su and Junyu Han.
> BMVC, pages 43.1-43.13, 2016
Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
|Model|Backbone|ACC|config|Download link|
| --- | --- | --- | --- | --- |
|---|---|---|---|---|
|StarNet|Resnet34_vd|84.44%|[configs/rec/rec_r34_vd_tps_bilstm_ctc.yml](../../configs/rec/rec_r34_vd_tps_bilstm_ctc.yml)|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar)|
|StarNet|MobileNetV3|81.42%|[configs/rec/rec_mv3_tps_bilstm_ctc.yml](../../configs/rec/rec_mv3_tps_bilstm_ctc.yml)|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_ctc_v2.0_train.tar)|
<a name="2"></a>
## 2. Environment
Please refer to ["Environment Preparation"](./environment.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone.md) to clone the project code.
<a name="3"></a>
## 3. Model Training / Evaluation / Prediction
Please refer to [Text Recognition Tutorial](./recognition.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
Training:
Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
```
#Single GPU training (long training period, not recommended)
python3 tools/train.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml
#Multi GPU training, specify the gpu number through the --gpus parameter
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c rec_r34_vd_tps_bilstm_ctc.yml
```
Evaluation:
```
# GPU evaluation
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
```
Prediction:
```
# The configuration file used for prediction must match the training
python3 tools/infer_rec.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
```
<a name="4"></a>
## 4. Inference and Deployment
<a name="4-1"></a>
### 4.1 Python Inference
First, the model saved during the STAR-Net text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_STAR-Net_train.tar) ), you can use the following command to convert:
```
python3 tools/export_model.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_tps_bilstm_ctc_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_starnet
```
For STAR-Net text recognition model inference, the following commands can be executed:
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rec_starnet/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
```
<a name="4-2"></a>
### 4.2 C++ Inference
With the inference model prepared, refer to the [cpp infer](../../deploy/cpp_infer/) tutorial for C++ inference.
<a name="4-3"></a>
### 4.3 Serving
With the inference model prepared, refer to the [pdserving](../../deploy/pdserving/) tutorial for service deployment by Paddle Serving.
<a name="4-4"></a>
### 4.4 More
More deployment schemes supported for STAR-Net:
- Paddle2ONNX: with the inference model prepared, please refer to the [paddle2onnx](../../deploy/paddle2onnx/) tutorial.
<a name="5"></a>
## 5. FAQ
## Citation
```bibtex
@inproceedings{liu2016star,
title={STAR-Net: a spatial attention residue network for scene text recognition.},
author={Liu, Wei and Chen, Chaofeng and Wong, Kwan-Yee K and Su, Zhizhong and Han, Junyu},
booktitle={BMVC},
volume={2},
pages={7},
year={2016}
}
```
# CRNN
- [1. Introduction](#1)
- [2. Environment](#2)
- [3. Model Training / Evaluation / Prediction](#3)
- [3.1 Training](#3-1)
- [3.2 Evaluation](#3-2)
- [3.3 Prediction](#3-3)
- [4. Inference and Deployment](#4)
- [4.1 Python Inference](#4-1)
- [4.2 C++ Inference](#4-2)
- [4.3 Serving](#4-3)
- [4.4 More](#4-4)
- [5. FAQ](#5)
<a name="1"></a>
## 1. Introduction
Paper:
> [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://arxiv.org/abs/1507.05717)
> Baoguang Shi, Xiang Bai, Cong Yao
> IEEE, 2015
Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
|Model|Backbone|ACC|config|Download link|
| --- | --- | --- | --- | --- |
|---|---|---|---|---|
|CRNN|Resnet34_vd|81.04%|[configs/rec/rec_r34_vd_none_bilstm_ctc.yml](../../configs/rec/rec_r34_vd_none_bilstm_ctc.yml)|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar)|
|CRNN|MobileNetV3|77.95%|[configs/rec/rec_mv3_none_bilstm_ctc.yml](../../configs/rec/rec_mv3_none_bilstm_ctc.yml)|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar)|
<a name="2"></a>
## 2. Environment
Please refer to ["Environment Preparation"](./environment.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone.md) to clone the project code.
<a name="3"></a>
## 3. Model Training / Evaluation / Prediction
Please refer to [Text Recognition Tutorial](./recognition.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
Training:
Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
```
#Single GPU training (long training period, not recommended)
python3 tools/train.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml
#Multi GPU training, specify the gpu number through the --gpus parameter
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml
```
Evaluation:
```
# GPU evaluation
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
```
Prediction:
```
# The configuration file used for prediction must match the training
python3 tools/infer_rec.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
```
<a name="4"></a>
## 4. Inference and Deployment
<a name="4-1"></a>
### 4.1 Python Inference
First, the model saved during the CRNN text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_CRNN_train.tar) ), you can use the following command to convert:
```
python3 tools/export_model.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_crnn
```
For CRNN text recognition model inference, the following commands can be executed:
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rec_crnn/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
```
<a name="4-2"></a>
### 4.2 C++ Inference
With the inference model prepared, refer to the [cpp infer](../../deploy/cpp_infer/) tutorial for C++ inference.
<a name="4-3"></a>
### 4.3 Serving
With the inference model prepared, refer to the [pdserving](../../deploy/pdserving/) tutorial for service deployment by Paddle Serving.
<a name="4-4"></a>
### 4.4 More
More deployment schemes supported for CRNN:
- Paddle2ONNX: with the inference model prepared, please refer to the [paddle2onnx](../../deploy/paddle2onnx/) tutorial.
<a name="5"></a>
## 5. FAQ
## Citation
```bibtex
@ARTICLE{7801919,
author={Shi, Baoguang and Bai, Xiang and Yao, Cong},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition},
year={2017},
volume={39},
number={11},
pages={2298-2304},
doi={10.1109/TPAMI.2016.2646371}}
```
# SEED
- [1. Introduction](#1)
- [2. Environment](#2)
- [3. Model Training / Evaluation / Prediction](#3)
- [3.1 Training](#3-1)
- [3.2 Evaluation](#3-2)
- [3.3 Prediction](#3-3)
- [4. Inference and Deployment](#4)
- [4.1 Python Inference](#4-1)
- [4.2 C++ Inference](#4-2)
- [4.3 Serving](#4-3)
- [4.4 More](#4-4)
- [5. FAQ](#5)
<a name="1"></a>
## 1. Introduction
Paper:
> [SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition](https://arxiv.org/pdf/2005.10977.pdf)
> Qiao, Zhi and Zhou, Yu and Yang, Dongbao and Zhou, Yucan and Wang, Weiping
> CVPR, 2020
Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
|Model|Backbone|ACC|config|Download link|
| --- | --- | --- | --- | --- |
|SEED|Aster_Resnet| 85.2% | [configs/rec/rec_resnet_stn_bilstm_att.yml](../../configs/rec/rec_resnet_stn_bilstm_att.yml) | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) |
<a name="2"></a>
## 2. Environment
Please refer to ["Environment Preparation"](./environment.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone.md) to clone the project code.
<a name="3"></a>
## 3. Model Training / Evaluation / Prediction
Please refer to [Text Recognition Tutorial](./recognition.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
Training:
The SEED model needs to additionally load the [language model](https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.en.300.bin.gz) trained by FastText, and install the fasttext dependencies:
```
python3 -m pip install fasttext==0.9.1
```
Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
```
#Single GPU training (long training period, not recommended)
python3 tools/train.py -c configs/rec/rec_resnet_stn_bilstm_att.yml
#Multi GPU training, specify the gpu number through the --gpus parameter
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c rec_resnet_stn_bilstm_att.yml
```
Evaluation:
```
# GPU evaluation
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_resnet_stn_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
```
Prediction:
```
# The configuration file used for prediction must match the training
python3 tools/infer_rec.py -c configs/rec/rec_resnet_stn_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
```
<a name="4"></a>
## 4. Inference and Deployment
<a name="4-1"></a>
### 4.1 Python Inference
Not support
<a name="4-2"></a>
### 4.2 C++ Inference
Not support
<a name="4-3"></a>
### 4.3 Serving
Not support
<a name="4-4"></a>
### 4.4 More
Not support
<a name="5"></a>
## 5. FAQ
## Citation
```bibtex
@inproceedings{qiao2020seed,
title={Seed: Semantics enhanced encoder-decoder framework for scene text recognition},
author={Qiao, Zhi and Zhou, Yu and Yang, Dongbao and Zhou, Yucan and Wang, Weiping},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={13528--13537},
year={2020}
}
```
......@@ -28,7 +28,34 @@
To prepare datasets, refer to [ocr_datasets](./dataset/ocr_datasets.md) .
If you want to reproduce the paper SAR, you need to download extra dataset [SynthAdd](https://pan.baidu.com/share/init?surl=uV0LtoNmcxbO-0YA7Ch4dg), extraction code: 627x. Besides, icdar2013, icdar2015, cocotext, IIIT5k datasets are also used to train. For specific details, please refer to the paper SAR.
PaddleOCR provides label files for training the icdar2015 dataset, which can be downloaded in the following ways:
```
# Training set label
wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_train.txt
# Test Set Label
wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt
```
PaddleOCR also provides a data format conversion script, which can convert ICDAR official website label to a data format
supported by PaddleOCR. The data conversion tool is in `ppocr/utils/gen_label.py`, here is the training set as an example:
```
# convert the official gt to rec_gt_label.txt
python gen_label.py --mode="rec" --input_path="{path/of/origin/label}" --output_label="rec_gt_label.txt"
```
The data format is as follows, (a) is the original picture, (b) is the Ground Truth text file corresponding to each picture:
![](../datasets/icdar_rec.png)
- Multilingual dataset
The multi-language model training method is the same as the Chinese model. The training data set is 100w synthetic data. A small amount of fonts and test data can be downloaded using the following two methods.
* [Baidu Netdisk](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA) ,Extraction code:frgi.
* [Google drive](https://drive.google.com/file/d/18cSWX7wXSy4G0tbKJ0d9PuIaiwRLHpjA/view)
<a name="Dictionary"></a>
### 1.2 Dictionary
......@@ -101,11 +128,11 @@ First download the pretrain model, you can download the trained model to finetun
```
cd PaddleOCR/
# Download the pre-trained model of MobileNetV3
wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar
# Download the pre-trained model of en_PP-OCRv3
wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_train.tar
# Decompress model parameters
cd pretrain_models
tar -xf rec_mv3_none_bilstm_ctc_v2.0_train.tar && rm -rf rec_mv3_none_bilstm_ctc_v2.0_train.tar
tar -xf en_PP-OCRv3_rec_train.tar && rm -rf en_PP-OCRv3_rec_train.tar
```
Start training:
......@@ -115,9 +142,10 @@ Start training:
# Training icdar15 English data and The training log will be automatically saved as train.log under "{save_model_dir}"
#specify the single card training(Long training time, not recommended)
python3 tools/train.py -c configs/rec/rec_icdar15_train.yml
python3 tools/train.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model=en_PP-OCRv3_rec_train/best_accuracy
#specify the card number through --gpus
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_icdar15_train.yml
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model=en_PP-OCRv3_rec_train/best_accuracy
```
......@@ -125,31 +153,13 @@ PaddleOCR supports alternating training and evaluation. You can modify `eval_bat
If the evaluation set is large, the test will be time-consuming. It is recommended to reduce the number of evaluations, or evaluate after training.
* Tip: You can use the `-c` parameter to select multiple model configurations under the `configs/rec/` path for training. The recognition algorithms supported by PaddleOCR are:
| Configuration file | Algorithm | backbone | trans | seq | pred |
| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: |
| [rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml) | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc |
| [rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml) | CRNN | ResNet34_vd | None | BiLSTM | ctc |
| rec_chinese_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc |
| rec_chinese_common_train.yml | CRNN | ResNet34_vd | None | BiLSTM | ctc |
| rec_icdar15_train.yml | CRNN | Mobilenet_v3 large 0.5 | None | BiLSTM | ctc |
| rec_mv3_none_bilstm_ctc.yml | CRNN | Mobilenet_v3 large 0.5 | None | BiLSTM | ctc |
| rec_mv3_none_none_ctc.yml | Rosetta | Mobilenet_v3 large 0.5 | None | None | ctc |
| rec_r34_vd_none_bilstm_ctc.yml | CRNN | Resnet34_vd | None | BiLSTM | ctc |
| rec_r34_vd_none_none_ctc.yml | Rosetta | Resnet34_vd | None | None | ctc |
| rec_mv3_tps_bilstm_att.yml | CRNN | Mobilenet_v3 | TPS | BiLSTM | att |
| rec_r34_vd_tps_bilstm_att.yml | CRNN | Resnet34_vd | TPS | BiLSTM | att |
| rec_r50fpn_vd_none_srn.yml | SRN | Resnet50_fpn_vd | None | rnn | srn |
| rec_mtb_nrtr.yml | NRTR | nrtr_mtb | None | transformer encoder | transformer decoder |
| rec_r31_sar.yml | SAR | ResNet31 | None | LSTM encoder | LSTM decoder |
* Tip: You can use the `-c` parameter to select multiple model configurations under the `configs/rec/` path for training. The recognition algorithms supported at [rec_algorithm](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_en/algorithm_overview.md):
For training Chinese data, it is recommended to use
[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml). If you want to try the result of other algorithms on the Chinese data set, please refer to the following instructions to modify the configuration file:
co
Take `rec_chinese_lite_train_v2.0.yml` as an example:
[ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml). If you want to try the result of other algorithms on the Chinese data set, please refer to the following instructions to modify the configuration file:
Take `ch_PP-OCRv3_rec_distillation.yml` as an example:
```
Global:
...
......@@ -183,7 +193,7 @@ Train:
...
- RecResizeImg:
# Modify image_shape to fit long text
image_shape: [3, 32, 320]
image_shape: [3, 48, 320]
...
loader:
...
......@@ -203,7 +213,7 @@ Eval:
...
- RecResizeImg:
# Modify image_shape to fit long text
image_shape: [3, 32, 320]
image_shape: [3, 48, 320]
...
loader:
# Eval batch_size for Single card
......@@ -380,11 +390,12 @@ Running on a DCU device requires setting the environment variable `export HIP_VI
<a name="31-evaluation"></a>
### 3.1 Evaluation
The model parameters during training are saved in the `Global.save_model_dir` directory by default. When evaluating indicators, you need to set `Global.checkpoints` to point to the saved parameter file. The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/rec/rec_icdar15_train.yml` file.
The model parameters during training are saved in the `Global.save_model_dir` directory by default. When evaluating indicators, you need to set `Global.checkpoints` to point to the saved parameter file. The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml` file.
```
# GPU evaluation, Global.checkpoints is the weight to be tested
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.checkpoints={path/to/weights}/best_accuracy
```
<a name="32-test"></a>
......@@ -417,7 +428,7 @@ Among them, best_accuracy.* is the best model on the evaluation set; iter_epoch_
```
# Predict English results
python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/en/word_1.jpg
python3 tools/infer_rec.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
```
......@@ -436,7 +447,7 @@ The configuration file used for prediction must be consistent with the training.
```
# Predict Chinese results
python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/ch/word_1.jpg
python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/ch/word_1.jpg
```
Input image:
......@@ -467,7 +478,7 @@ The recognition model is converted to the inference model in the same way as the
# Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
# Global.save_inference_dir Set the address where the converted model will be saved.
python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.save_inference_dir=./inference/rec_crnn/
python3 tools/export_model.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model=en_PP-OCRv3_rec_train/best_accuracy Global.save_inference_dir=./inference/en_PP-OCRv3_rec/
```
If you have a model trained on your own dataset with a different dictionary file, please make sure that you modify the `character_dict_path` in the configuration file to your dictionary file path.
......@@ -475,7 +486,8 @@ If you have a model trained on your own dataset with a different dictionary file
After the conversion is successful, there are three files in the model save directory:
```
inference/rec_crnn/
inference/en_PP-OCRv3_rec/
├── inference.pdiparams # The parameter file of recognition inference model
├── inference.pdiparams.info # The parameter information of recognition inference model, which can be ignored
└── inference.pdmodel # The program file of recognition model
......
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