From 921d0907d20a7ae121aa0980a8356caff4abc1b5 Mon Sep 17 00:00:00 2001 From: Jethong <1147925384@qq.com> Date: Thu, 8 Apr 2021 22:27:58 +0800 Subject: [PATCH] add pgnet.md --- doc/doc_ch/e2e.md | 116 -------------- doc/doc_ch/pgnet.md | 176 ++++++++++++++++++++++ doc/imgs_results/e2e_res_img293_pgnet.png | Bin 0 -> 678719 bytes doc/imgs_results/e2e_res_img295_pgnet.png | Bin 0 -> 478011 bytes doc/pgnet_framework.png | Bin 0 -> 247463 bytes train_data/total_text/train/poly/2.txt | 2 - train_data/total_text/train/rgb/2.jpg | Bin 41876 -> 0 bytes 7 files changed, 176 insertions(+), 118 deletions(-) delete mode 100644 doc/doc_ch/e2e.md create mode 100644 doc/doc_ch/pgnet.md create mode 100644 doc/imgs_results/e2e_res_img293_pgnet.png create mode 100644 doc/imgs_results/e2e_res_img295_pgnet.png create mode 100644 doc/pgnet_framework.png delete mode 100644 train_data/total_text/train/poly/2.txt delete mode 100644 train_data/total_text/train/rgb/2.jpg diff --git a/doc/doc_ch/e2e.md b/doc/doc_ch/e2e.md deleted file mode 100644 index e1909604..00000000 --- a/doc/doc_ch/e2e.md +++ /dev/null @@ -1,116 +0,0 @@ -# 端到端文字识别 - -本节以partvgg/totaltext数据集为例,介绍PaddleOCR中端到端模型的训练、评估与测试。 - -## 数据准备 -支持两种不同的数据形式textnet / icdar ,分别为四点标注数据和十四点标注数据,十四点标注数据效果要比四点标注效果好 -###数据形式为textnet - -解压数据集和下载标注文件后,PaddleOCR/train_data/part_vgg_synth/train/ 有一个文件夹和一个文件,分别是: -``` -/PaddleOCR/train_data/part_vgg_synth/train/ - |- image/ partvgg数据集的训练数据 - |- 119_nile_110_31.png - | ... - |- train_annotation_info.txt partvgg数据集的测试标注 -``` - -提供的标注文件格式如下,中间用"\t"分隔: -``` -" 图像文件名 图像标注信息--四点标注 图像标注信息--识别标注 -119_nile_110_31 140.2 222.5 266.0 194.6 278.7 251.8 152.9 279.7 Path: 32.9 133.1 106.0 130.8 106.4 143.8 33.3 146.1 were 21.8 81.9 106.9 80.4 107.7 123.2 22.6 124.7 why -``` -标注文件txt当中,其中每一行代表一组数据,以第一行为例。第一个代表同级目录image/下面的文件名前缀, 后面每9个代表一组标注信息,前8个代表文本框的四个点坐标(x,y),从左上角的点开始顺时针排列。 -最后一个代表文字的识别结果,**当其内容为“###”时,表示该文本框无效,在训练时会跳过。** - - -###数据形式为icdar -解压数据集和下载标注文件后,PaddleOCR/train_data/total_text/train/ 有两个文件夹,分别是: -``` -/PaddleOCR/train_data/total_text/train/ - |- rgb/ total_text数据集的训练数据 - |- gt_0.png - | ... - |- poly/ total_text数据集的测试标注 - |- gt_0.txt - | ... -``` - -提供的标注文件格式如下,中间用"\t"分隔: -``` -" 图像标注信息--十四点标注数据 图像标注信息--识别标注 -1004.0,689.0,1019.0,698.0,1034.0,708.0,1049.0,718.0,1064.0,728.0,1079.0,738.0,1095.0,748.0,1094.0,774.0,1079.0,765.0,1065.0,756.0,1050.0,747.0,1036.0,738.0,1021.0,729.0,1007.0,721.0 EST -1102.0,755.0,1116.0,764.0,1131.0,773.0,1146.0,783.0,1161.0,792.0,1176.0,801.0,1191.0,811.0,1193.0,837.0,1178.0,828.0,1164.0,819.0,1150.0,810.0,1135.0,801.0,1121.0,792.0,1107.0,784.0 1972 -``` -标注文件当中,其中每一个txt文件代表一组数据,文件名就是同级目录rgb/下面的文件名。以第一行为例,前面28个代表文本框的十四个点坐标(x,y),从左上角的点开始顺时针排列。 -最后一个代表文字的识别结果,**当其内容为“###”时,表示该文本框无效,在训练时会跳过。** -如果您想在其他数据集上训练,可以按照上述形式构建标注文件。 - -## 快速启动训练 - -首先下载模型backbone的pretrain model,PaddleOCR的检测模型目前支持两种backbone,分别是MobileNetV3、ResNet_vd系列, -您可以根据需求使用[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures)中的模型更换backbone。 -```shell -cd PaddleOCR/ -下载ResNet50_vd的动态图预训练模型 -wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams - -./pretrain_models/ - └─ ResNet50_vd_ssld_pretrained.pdparams - -``` - -#### 启动训练 - -*如果您安装的是cpu版本,请将配置文件中的 `use_gpu` 字段修改为false* - -```shell -# 单机单卡训练 e2e 模型 -python3 tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./pretrain_models/ResNet50_vd_ssld_pretrained Global.load_static_weights=False -# 单机多卡训练,通过 --gpus 参数设置使用的GPU ID -python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./pretrain_models/ResNet50_vd_ssld_pretrained Global.load_static_weights=False -``` - - -上述指令中,通过-c 选择训练使用configs/e2e/e2e_r50_vd_pg.yml配置文件。 -有关配置文件的详细解释,请参考[链接](./config.md)。 - -您也可以通过-o参数在不需要修改yml文件的情况下,改变训练的参数,比如,调整训练的学习率为0.0001 -```shell -python3 tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Optimizer.base_lr=0.0001 -``` - -#### 断点训练 - -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/e2e/e2e_r50_vd_pg.yml -o Global.checkpoints=./your/trained/model -``` - -**注意**:`Global.checkpoints`的优先级高于`Global.pretrain_weights`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.pretrain_weights`指定的模型。 - -## 指标评估 - -PaddleOCR计算三个OCR端到端相关的指标,分别是:Precision、Recall、Hmean。 - -运行如下代码,根据配置文件`e2e_r50_vd_pg.yml`中`save_res_path`指定的测试集检测结果文件,计算评估指标。 - -评估时设置后处理参数`max_side_len=768`,使用不同数据集、不同模型训练,可调整参数进行优化 -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。 -```shell -python3 tools/eval.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.checkpoints="{path/to/weights}/best_accuracy" -``` - - - -## 测试端到端效果 - -测试单张图像的端到端识别效果 -```shell -python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false -``` - -测试文件夹下所有图像的端到端识别效果 -```shell -python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false -``` diff --git a/doc/doc_ch/pgnet.md b/doc/doc_ch/pgnet.md new file mode 100644 index 00000000..ff998327 --- /dev/null +++ b/doc/doc_ch/pgnet.md @@ -0,0 +1,176 @@ +# 端对端OCR算法-PGNet +- [一、简介](#简介) +- [二、环境配置](#环境配置) +- [三、快速使用](#快速使用) +- [四、快速训练](#开始训练) +- [五、预测推理](#预测推理) + + + +##简介 +OCR算法可以分为两阶段算法和端对端的算法。二阶段OCR算法一般分为两个部分,文本检测和文本识别算法,文件检测算法从图像中得到文本行的检测框,然后识别算法去识别文本框中的内容。而端对端OCR算法可以在一个算法中完成文字检测和文字识别,其基本思想是设计一个同时具有检测单元和识别模块的模型,共享其中两者的CNN特征,并联合训练。由于一个算法即可完成文字识别,端对端模型更小,速度更快。 + +### PGNet算法介绍 +近些年来,端对端OCR算法得到了良好的发展,包括MaskTextSpotter系列、TextSnake、TextDragon、PGNet系列等算法。在这些算法中,PGNet算法具备其他算法不具备的优势,包括: +- 设计PGNet loss指导训练,不需要字符级别的标注 +- 不需要NMS和ROI相关操作,加速预测 +- 提出预测文本行内的阅读顺序模块; +- 提出基于图的修正模块(GRM)来进一步提高模型识别性能 +- 精度更高,预测速度更快 + +PGNet算法细节详见[论文](https://www.aaai.org/AAAI21Papers/AAAI-2885.WangP.pdf), 算法原理图如下所示: +![](../pgnet_framework.png) +输入图像经过特征提取送入四个分支,分别是:文本边缘偏移量预测TBO模块,文本中心线预测TCL模块,文本方向偏移量预测TDO模块,以及文本字符分类图预测TCC模块。 +其中TBO以及TCL的输出经过后处理后可以得到文本的检测结果,TCL、TDO、TCC负责文本识别。 +其检测识别效果图如下: +![](../imgs_results/e2e_res_img293_pgnet.png) +![](../imgs_results/e2e_res_img295_pgnet.png) + + +##环境配置 +请先参考[快速安装](./installation.md)配置PaddleOCR运行环境。 + +*注意:也可以通过 whl 包安装使用PaddleOCR,具体参考[Paddleocr Package使用说明](./whl.md)。* + + +##快速使用 +### inference模型下载 +本节以训练好的端到端模型为例,快速使用模型预测,首先下载训练好的端到端inference模型[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/e2e_server_pgnetA_infer.tar) +``` +mkdir inference && cd inference +# 下载英文端到端模型并解压 +wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/e2e_server_pgnetA_infer.tar && tar xf e2e_server_pgnetA_infer.tar +``` +* windows 环境下如果没有安装wget,下载模型时可将链接复制到浏览器中下载,并解压放置在相应目录下 + +解压完毕后应有如下文件结构: +``` +├── e2e_server_pgnetA_infer +│ ├── inference.pdiparams +│ ├── inference.pdiparams.info +│ └── inference.pdmodel +``` +### 单张图像或者图像集合预测 +```bash +# 预测image_dir指定的单张图像 +python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=True + +# 预测image_dir指定的图像集合 +python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=True + +# 如果想使用CPU进行预测,需设置use_gpu参数为False +python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=True --use_gpu=False +``` + +##开始训练 +本节以totaltext数据集为例,介绍PaddleOCR中端到端模型的训练、评估与测试。 +###数据形式为icdar, 十六点标注数据 +解压数据集和下载标注文件后,PaddleOCR/train_data/total_text/train/ 有两个文件夹,分别是: +``` +/PaddleOCR/train_data/total_text/train/ + |- rgb/ total_text数据集的训练数据 + |- gt_0.png + | ... + |- total_text.txt total_text数据集的训练标注 +``` + +提供的标注文件格式如下,中间用"\t"分隔: +``` +" 图像文件名 json.dumps编码的图像标注信息" +rgb/gt_0.png [{"transcription": "EST", "points": [[1004.0,689.0],[1019.0,698.0],[1034.0,708.0],[1049.0,718.0],[1064.0,728.0],[1079.0,738.0],[1095.0,748.0],[1094.0,774.0],[1079.0,765.0],[1065.0,756.0],[1050.0,747.0],[1036.0,738.0],[1021.0,729.0],[1007.0,721.0]]}, {...}] +``` +json.dumps编码前的图像标注信息是包含多个字典的list,字典中的 `points` 表示文本框的四个点的坐标(x, y),从左上角的点开始顺时针排列。 +`transcription` 表示当前文本框的文字,**当其内容为“###”时,表示该文本框无效,在训练时会跳过。** +如果您想在其他数据集上训练,可以按照上述形式构建标注文件。 + +### 快速启动训练 + +模型训练一般分两步骤进行,第一步可以选择用合成数据训练,第二步加载第一步训练好的模型训练,这边我们提供了第一步训练好的模型,可以直接加载,从第二步开始训练 +[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/train_step1.tar) +```shell +cd PaddleOCR/ +下载ResNet50_vd的动态图预训练模型 +wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/train_step1.tar +可以得到以下的文件格式 +./pretrain_models/train_step1/ + └─ best_accuracy.pdopt + └─ best_accuracy.states + └─ best_accuracy.pdparams + +``` + +*如果您安装的是cpu版本,请将配置文件中的 `use_gpu` 字段修改为false* + +```shell +# 单机单卡训练 e2e 模型 +python3 tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./pretrain_models/train_step1/best_accuracy Global.load_static_weights=False +# 单机多卡训练,通过 --gpus 参数设置使用的GPU ID +python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./pretrain_models/train_step1/best_accuracy Global.load_static_weights=False +``` + +上述指令中,通过-c 选择训练使用configs/e2e/e2e_r50_vd_pg.yml配置文件。 +有关配置文件的详细解释,请参考[链接](./config.md)。 + +您也可以通过-o参数在不需要修改yml文件的情况下,改变训练的参数,比如,调整训练的学习率为0.0001 +```shell +python3 tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Optimizer.base_lr=0.0001 +``` + +#### 断点训练 + +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/e2e/e2e_r50_vd_pg.yml -o Global.checkpoints=./your/trained/model +``` + +**注意**:`Global.checkpoints`的优先级高于`Global.pretrain_weights`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.pretrain_weights`指定的模型。 + + +## 预测推理 + +PaddleOCR计算三个OCR端到端相关的指标,分别是:Precision、Recall、Hmean。 + +运行如下代码,根据配置文件`e2e_r50_vd_pg.yml`中`save_res_path`指定的测试集检测结果文件,计算评估指标。 + +评估时设置后处理参数`max_side_len=768`,使用不同数据集、不同模型训练,可调整参数进行优化 +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。 +```shell +python3 tools/eval.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.checkpoints="{path/to/weights}/best_accuracy" +``` + +### 测试端到端效果 +测试单张图像的端到端识别效果 +```shell +python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false +``` + +测试文件夹下所有图像的端到端识别效果 +```shell +python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false +``` + +###转为推理模型 +### (1). 四边形文本检测模型(ICDAR2015) +首先将PGNet端到端训练过程中保存的模型,转换成inference model。以基于Resnet50_vd骨干网络,以英文数据集训练的模型为例[模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar) ,可以使用如下命令进行转换: +``` +wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar && tar xf en_server_pgnetA.tar +python3 tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./en_server_pgnetA/iter_epoch_450 Global.load_static_weights=False Global.save_inference_dir=./inference/e2e +``` +**PGNet端到端模型推理,需要设置参数`--e2e_algorithm="PGNet"`**,可以执行如下命令: +``` +python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img_10.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=False +``` +可视化文本检测结果默认保存到`./inference_results`文件夹里面,结果文件的名称前缀为'e2e_res'。结果示例如下: + +![](../imgs_results/e2e_res_img_10_pgnet.jpg) + +### (2). 弯曲文本检测模型(Total-Text) +对于弯曲文本样例 + +**PGNet端到端模型推理,需要设置参数`--e2e_algorithm="PGNet"`,同时,还需要增加参数`--e2e_pgnet_polygon=True`,**可以执行如下命令: +``` +python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=True +``` +可视化文本端到端结果默认保存到`./inference_results`文件夹里面,结果文件的名称前缀为'e2e_res'。结果示例如下: + +![](../imgs_results/e2e_res_img623_pgnet.jpg) diff --git a/doc/imgs_results/e2e_res_img293_pgnet.png b/doc/imgs_results/e2e_res_img293_pgnet.png new file mode 100644 index 0000000000000000000000000000000000000000..232f8293adbe00c46b1ea14e6206c3b0eb31ca8f GIT binary patch literal 678719 zcmV)YK&-!sP)!e$InRgts_N?6)z#Gnzg|_nPqZflQsi_dRt&^0z5;QoVqq;(M=KiE{R#n;?ef1uIk zGwDCr*umz;L&@*X1J3h~CJx1YaF@>AQ*>_w+v_{ny1PxD<@mk#$d_`;W zeh*vs*0FVelk%pP-#X=s@=9f}dHqK$aOY1xt^`#-uiuQ8M3>v#n0^7d(pi=wNCKw 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