From 78871cfefbe6d4bafefe76912ead88b1a83a5309 Mon Sep 17 00:00:00 2001 From: littletomatodonkey Date: Mon, 15 Aug 2022 11:39:11 +0800 Subject: [PATCH] kie doc (#7154) * kie doc * fix xlm model export * fix doc * add wildreceipt dataset * fix doc * fix doc --- ...e854b8817dedb8585e0732089fd1f752d2cec.jpeg | Bin 0 -> 185497 bytes doc/datasets/wildreceipt_demo/2769.jpeg | Bin 0 -> 178786 bytes doc/doc_ch/algorithm_kie_layoutxlm.md | 172 +++++++ doc/doc_ch/algorithm_kie_sdmgr.md | 144 ++++++ doc/doc_ch/algorithm_kie_vi_layoutxlm.md | 166 +++++++ doc/doc_ch/algorithm_overview.md | 38 +- .../{docvqa_datasets.md => kie_datasets.md} | 31 +- doc/doc_ch/kie.md | 463 ++++++++++++++++++ .../doc_en/algorithm_sdmgr_en.md | 0 ppocr/modeling/backbones/vqa_layoutlm.py | 9 +- ppocr/utils/save_load.py | 5 +- .../docs/imgs/{0.png => sdmgr_result.png} | Bin ppstructure/docs/kie.md | 71 --- .../docs/vqa/result_re/zh_val_42_re.jpg | Bin 0 -> 1679480 bytes .../result_re_with_gt_ocr/zh_val_42_re.jpg | Bin 0 -> 1651474 bytes .../docs/vqa/result_ser/zh_val_42_ser.jpg | Bin 1707291 -> 1609020 bytes .../result_ser_with_gt_ocr/zh_val_42_ser.jpg | Bin 0 -> 1620325 bytes ppstructure/utility.py | 2 + ppstructure/vqa/predict_vqa_token_ser.py | 6 +- tools/export_model.py | 2 +- tools/infer/utility.py | 18 +- tools/infer_vqa_token_ser.py | 2 - 22 files changed, 1035 insertions(+), 94 deletions(-) create mode 100644 doc/datasets/wildreceipt_demo/1bbe854b8817dedb8585e0732089fd1f752d2cec.jpeg create mode 100644 doc/datasets/wildreceipt_demo/2769.jpeg create mode 100644 doc/doc_ch/algorithm_kie_layoutxlm.md create mode 100644 doc/doc_ch/algorithm_kie_sdmgr.md create mode 100644 doc/doc_ch/algorithm_kie_vi_layoutxlm.md rename doc/doc_ch/dataset/{docvqa_datasets.md => kie_datasets.md} (60%) create mode 100644 doc/doc_ch/kie.md rename ppstructure/docs/kie_en.md => doc/doc_en/algorithm_sdmgr_en.md (100%) rename ppstructure/docs/imgs/{0.png => sdmgr_result.png} (100%) delete mode 100644 ppstructure/docs/kie.md 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+ +
+ + + +### 4.2 C++推理部署 + +暂不支持 + + +### 4.3 Serving服务化部署 + +暂不支持 + + +### 4.4 更多推理部署 + +暂不支持 + + + +## 5. FAQ + +## 引用 + + +```bibtex +@article{DBLP:journals/corr/abs-2104-08836, + author = {Yiheng Xu and + Tengchao Lv and + Lei Cui and + Guoxin Wang and + Yijuan Lu and + Dinei Flor{\^{e}}ncio and + Cha Zhang and + Furu Wei}, + title = {LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich + Document Understanding}, + journal = {CoRR}, + volume = {abs/2104.08836}, + year = {2021}, + url = {https://arxiv.org/abs/2104.08836}, + eprinttype = {arXiv}, + eprint = {2104.08836}, + timestamp = {Thu, 14 Oct 2021 09:17:23 +0200}, + biburl = {https://dblp.org/rec/journals/corr/abs-2104-08836.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} + +@article{DBLP:journals/corr/abs-1912-13318, + author = {Yiheng Xu and + Minghao Li and + Lei Cui and + Shaohan Huang and + Furu Wei and + Ming Zhou}, + title = {LayoutLM: Pre-training of Text and Layout for Document Image Understanding}, + journal = {CoRR}, + volume = {abs/1912.13318}, + year = {2019}, + url = {http://arxiv.org/abs/1912.13318}, + eprinttype = {arXiv}, + eprint = {1912.13318}, + timestamp = {Mon, 01 Jun 2020 16:20:46 +0200}, + biburl = {https://dblp.org/rec/journals/corr/abs-1912-13318.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} + +@article{DBLP:journals/corr/abs-2012-14740, + author = {Yang Xu and + Yiheng Xu and + Tengchao Lv and + Lei Cui and + Furu Wei and + Guoxin Wang and + Yijuan Lu and + Dinei A. F. Flor{\^{e}}ncio and + Cha Zhang and + Wanxiang Che and + Min Zhang and + Lidong Zhou}, + title = {LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding}, + journal = {CoRR}, + volume = {abs/2012.14740}, + year = {2020}, + url = {https://arxiv.org/abs/2012.14740}, + eprinttype = {arXiv}, + eprint = {2012.14740}, + timestamp = {Tue, 27 Jul 2021 09:53:52 +0200}, + biburl = {https://dblp.org/rec/journals/corr/abs-2012-14740.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} +``` diff --git a/doc/doc_ch/algorithm_kie_sdmgr.md b/doc/doc_ch/algorithm_kie_sdmgr.md new file mode 100644 index 00000000..10f3ca06 --- /dev/null +++ b/doc/doc_ch/algorithm_kie_sdmgr.md @@ -0,0 +1,144 @@ + +# 关键信息抽取算法-SDMGR + +- [1. 算法简介](#1-算法简介) +- [2. 环境配置](#2-环境配置) +- [3. 模型训练、评估、预测](#3-模型训练评估预测) + - [3.1 模型训练](#31-模型训练) + - [3.2 模型评估](#32-模型评估) + - [3.3 模型预测](#33-模型预测) +- [4. 推理部署](#4-推理部署) + - [4.1 Python推理](#41-python推理) + - [4.2 C++推理部署](#42-c推理部署) + - [4.3 Serving服务化部署](#43-serving服务化部署) + - [4.4 更多推理部署](#44-更多推理部署) +- [5. FAQ](#5-faq) +- [引用](#引用) + + + + +## 1. 算法简介 + + +论文信息: + +> [Spatial Dual-Modality Graph Reasoning for Key Information Extraction](https://arxiv.org/abs/2103.14470) +> +> Hongbin Sun and Zhanghui Kuang and Xiaoyu Yue and Chenhao Lin and Wayne Zhang +> +> 2021 + +在wildreceipt发票公开数据集上,算法复现效果如下: + +|模型|骨干网络|配置文件|hmean|下载链接| +| --- | --- | --- | --- | --- | +|SDMGR|VGG6|[configs/kie/sdmgr/kie_unet_sdmgr.yml](../../configs/kie/sdmgr/kie_unet_sdmgr.yml)|86.7%|[训练模型]( https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)/[推理模型(coming soon)]()| + + + + +## 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。 + + + + +## 3. 模型训练、评估、预测 + +SDMGR是一个关键信息提取算法,将每个检测到的文本区域分类为预定义的类别,如订单ID、发票号码,金额等。 + +训练和测试的数据采用wildreceipt数据集,通过如下指令下载数据集: + +```bash +wget https://paddleocr.bj.bcebos.com/ppstructure/dataset/wildreceipt.tar && tar xf wildreceipt.tar +``` + +创建数据集软链到PaddleOCR/train_data目录下: +``` +cd PaddleOCR/ && mkdir train_data && cd train_data + +ln -s ../../wildreceipt ./ +``` + + +### 3.1 模型训练 + +训练采用的配置文件是`configs/kie/sdmgr/kie_unet_sdmgr.yml`,配置文件中默认训练数据路径是`train_data/wildreceipt`,准备好数据后,可以通过如下指令执行训练: + +``` +python3 tools/train.py -c configs/kie/sdmgr/kie_unet_sdmgr.yml -o Global.save_model_dir=./output/kie/ +``` + +### 3.2 模型评估 + +执行下面的命令进行模型评估 + +```bash +python3 tools/eval.py -c configs/kie/sdmgr/kie_unet_sdmgr.yml -o Global.checkpoints=./output/kie/best_accuracy +``` + +输出信息示例如下所示。 + +```py +[2022/08/10 05:22:23] ppocr INFO: metric eval *************** +[2022/08/10 05:22:23] ppocr INFO: hmean:0.8670120239257812 +[2022/08/10 05:22:23] ppocr INFO: fps:10.18816520530961 +``` + +### 3.3 模型预测 + +执行下面的命令进行模型预测,预测的时候需要预先加载存储图片路径以及OCR信息的文本文件,使用`Global.infer_img`进行指定。 + +```bash +python3 tools/infer_kie.py -c configs/kie/kie_unet_sdmgr.yml -o Global.checkpoints=kie_vgg16/best_accuracy Global.infer_img=./train_data/wildreceipt/1.txt +``` + +执行预测后的结果保存在`./output/sdmgr_kie/predicts_kie.txt`文件中,可视化结果保存在`/output/sdmgr_kie/kie_results/`目录下。 + +可视化结果如下图所示: + +
+ +
+ + +## 4. 推理部署 + + +### 4.1 Python推理 + +暂不支持 + + +### 4.2 C++推理部署 + +暂不支持 + + +### 4.3 Serving服务化部署 + +暂不支持 + + +### 4.4 更多推理部署 + +暂不支持 + + + +## 5. FAQ + +## 引用 + + +```bibtex +@misc{sun2021spatial, + title={Spatial Dual-Modality Graph Reasoning for Key Information Extraction}, + author={Hongbin Sun and Zhanghui Kuang and Xiaoyu Yue and Chenhao Lin and Wayne Zhang}, + year={2021}, + eprint={2103.14470}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` diff --git a/doc/doc_ch/algorithm_kie_vi_layoutxlm.md b/doc/doc_ch/algorithm_kie_vi_layoutxlm.md new file mode 100644 index 00000000..155849a6 --- /dev/null +++ b/doc/doc_ch/algorithm_kie_vi_layoutxlm.md @@ -0,0 +1,166 @@ +# 关键信息抽取算法-VI-LayoutXLM + +- [1. 算法简介](#1-算法简介) +- [2. 环境配置](#2-环境配置) +- [3. 模型训练、评估、预测](#3-模型训练评估预测) +- [4. 推理部署](#4-推理部署) + - [4.1 Python推理](#41-python推理) + - [4.2 C++推理部署](#42-c推理部署) + - [4.3 Serving服务化部署](#43-serving服务化部署) + - [4.4 更多推理部署](#44-更多推理部署) +- [5. FAQ](#5-faq) +- [引用](#引用) + + + + +## 1. 算法简介 + +VI-LayoutXLM基于LayoutXLM进行改进,在下游任务训练过程中,去除视觉骨干网络模块,最终精度基本无损的情况下,模型推理速度进一步提升。 + +在XFUND_zh数据集上,算法复现效果如下: + +|模型|骨干网络|任务|配置文件|hmean|下载链接| +| --- | --- |---| --- | --- | --- | +|VI-LayoutXLM |VI-LayoutXLM-base | SER |[ser_vi_layoutxlm_xfund_zh_udml.yml](../../configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh_udml.yml)|93.19%|[训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_pretrained.tar)/[推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_infer.tar)| +|VI-LayoutXLM |VI-LayoutXLM-base |RE | [re_vi_layoutxlm_xfund_zh_udml.yml](../../configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh_udml.yml)|83.92%|[训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_pretrained.tar)/[推理模型(coming soon)]()| + + + +## 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。 + + + + +## 3. 模型训练、评估、预测 + +请参考[关键信息抽取教程](./kie.md)。PaddleOCR对代码进行了模块化,训练不同的关键信息抽取模型只需要**更换配置文件**即可。 + + + +## 4. 推理部署 + + + +### 4.1 Python推理 + +**注:** 目前RE任务推理过程仍在适配中,下面以SER任务为例,介绍基于VI-LayoutXLM模型的关键信息抽取过程。 + +首先将训练得到的模型转换成inference model。以VI-LayoutXLM模型在XFUND_zh数据集上训练的模型为例([模型下载地址](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_pretrained.tar)),可以使用下面的命令进行转换。 + +``` bash +wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_pretrained.tar +tar -xf ser_vi_layoutxlm_xfund_pretrained.tar +python3 tools/export_model.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./ser_vi_layoutxlm_xfund_pretrained/best_accuracy Global.save_inference_dir=./inference/ser_vi_layoutxlm_infer +``` + +VI-LayoutXLM模型基于SER任务进行推理,可以执行如下命令: + +```bash +cd ppstructure +python3 vqa/predict_vqa_token_ser.py \ + --vqa_algorithm=LayoutXLM \ + --ser_model_dir=../inference/ser_vi_layoutxlm_infer \ + --image_dir=./docs/vqa/input/zh_val_42.jpg \ + --ser_dict_path=../train_data/XFUND/class_list_xfun.txt \ + --vis_font_path=../doc/fonts/simfang.ttf \ + --ocr_order_method="tb-yx" +``` + +SER可视化结果默认保存到`./output`文件夹里面,结果示例如下: + +
+ +
+ + + +### 4.2 C++推理部署 + +暂不支持 + + +### 4.3 Serving服务化部署 + +暂不支持 + + +### 4.4 更多推理部署 + +暂不支持 + + + +## 5. FAQ + +## 引用 + + +```bibtex +@article{DBLP:journals/corr/abs-2104-08836, + author = {Yiheng Xu and + Tengchao Lv and + Lei Cui and + Guoxin Wang and + Yijuan Lu and + Dinei Flor{\^{e}}ncio and + Cha Zhang and + Furu Wei}, + title = {LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich + Document Understanding}, + journal = {CoRR}, + volume = {abs/2104.08836}, + year = {2021}, + url = {https://arxiv.org/abs/2104.08836}, + eprinttype = {arXiv}, + eprint = {2104.08836}, + timestamp = {Thu, 14 Oct 2021 09:17:23 +0200}, + biburl = {https://dblp.org/rec/journals/corr/abs-2104-08836.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} + +@article{DBLP:journals/corr/abs-1912-13318, + author = {Yiheng Xu and + Minghao Li and + Lei Cui and + Shaohan Huang and + Furu Wei and + Ming Zhou}, + title = {LayoutLM: Pre-training of Text and Layout for Document Image Understanding}, + journal = {CoRR}, + volume = {abs/1912.13318}, + year = {2019}, + url = {http://arxiv.org/abs/1912.13318}, + eprinttype = {arXiv}, + eprint = {1912.13318}, + timestamp = {Mon, 01 Jun 2020 16:20:46 +0200}, + biburl = {https://dblp.org/rec/journals/corr/abs-1912-13318.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} + +@article{DBLP:journals/corr/abs-2012-14740, + author = {Yang Xu and + Yiheng Xu and + Tengchao Lv and + Lei Cui and + Furu Wei and + Guoxin Wang and + Yijuan Lu and + Dinei A. F. Flor{\^{e}}ncio and + Cha Zhang and + Wanxiang Che and + Min Zhang and + Lidong Zhou}, + title = {LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding}, + journal = {CoRR}, + volume = {abs/2012.14740}, + year = {2020}, + url = {https://arxiv.org/abs/2012.14740}, + eprinttype = {arXiv}, + eprint = {2012.14740}, + timestamp = {Tue, 27 Jul 2021 09:53:52 +0200}, + biburl = {https://dblp.org/rec/journals/corr/abs-2012-14740.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} +``` diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md index 9d725a86..b96c1a3b 100755 --- a/doc/doc_ch/algorithm_overview.md +++ b/doc/doc_ch/algorithm_overview.md @@ -1,10 +1,11 @@ -# OCR算法 +# 算法汇总 -- [1. 两阶段算法](#1) +- [1. 两阶段OCR算法](#1) - [1.1 文本检测算法](#11) - [1.2 文本识别算法](#12) -- [2. 端到端算法](#2) +- [2. 端到端OCR算法](#2) - [3. 表格识别算法](#3) +- [4. 关键信息抽取算法](#4) 本文给出了PaddleOCR已支持的OCR算法列表,以及每个算法在**英文公开数据集**上的模型和指标,主要用于算法简介和算法性能对比,更多包括中文在内的其他数据集上的模型请参考[PP-OCR v2.0 系列模型下载](./models_list.md)。 @@ -114,3 +115,34 @@ |模型|骨干网络|配置文件|acc|下载链接| |---|---|---|---|---| |TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar) / [推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| + + + +## 4. 关键信息抽取算法 + +已支持的关键信息抽取算法列表(戳链接获取使用教程): + +- [x] [VI-LayoutXLM](./algorithm_kie_vi_laoutxlm.md) +- [x] [LayoutLM](./algorithm_kie_laoutxlm.md) +- [x] [LayoutLMv2](./algorithm_kie_laoutxlm.md) +- [x] [LayoutXLM](./algorithm_kie_laoutxlm.md) +- [x] [SDMGR](././algorithm_kie_sdmgr.md) + +在wildreceipt发票公开数据集上,算法复现效果如下: + +|模型|骨干网络|配置文件|hmean|下载链接| +| --- | --- | --- | --- | --- | +|SDMGR|VGG6|[configs/kie/sdmgr/kie_unet_sdmgr.yml](../../configs/kie/sdmgr/kie_unet_sdmgr.yml)|86.7%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)| + + +在XFUND_zh公开数据集上,算法效果如下: + +|模型|骨干网络|任务|配置文件|hmean|下载链接| +| --- | --- | --- | --- | --- | --- | +|VI-LayoutXLM| VI-LayoutXLM-base | SER | [ser_vi_layoutxlm_xfund_zh_udml.yml](../../configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh_udml.yml)|**93.19%**|[训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_pretrained.tar)| +|LayoutXLM| LayoutXLM-base | SER | [ser_layoutxlm_xfund_zh.yml](../../configs/kie/layoutlm_series/ser_layoutxlm_xfund_zh.yml)|90.38%|[训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar)| +|LayoutLM| LayoutLM-base | SER | [ser_layoutlm_xfund_zh.yml](../../configs/kie/layoutlm_series/ser_layoutlm_xfund_zh.yml)|77.31%|[训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar)| +|LayoutLMv2| LayoutLMv2-base | SER | [ser_layoutlmv2_xfund_zh.yml](../../configs/kie/layoutlm_series/ser_layoutlmv2_xfund_zh.yml)|85.44%|[训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar)| +|VI-LayoutXLM| VI-LayoutXLM-base | RE | [re_vi_layoutxlm_xfund_zh_udml.yml](../../configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh_udml.yml)|**83.92%**|[训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_pretrained.tar)| +|LayoutXLM| LayoutXLM-base | RE | [re_layoutxlm_xfund_zh.yml](../../configs/kie/layoutlm_series/re_layoutxlm_xfund_zh.yml)|74.83%|[训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar)| +|LayoutLMv2| LayoutLMv2-base | RE | [re_layoutlmv2_xfund_zh.yml](../../configs/kie/layoutlm_series/re_layoutlmv2_xfund_zh.yml)|67.77%|[训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutLMv2_xfun_zh.tar)| diff --git a/doc/doc_ch/dataset/docvqa_datasets.md b/doc/doc_ch/dataset/kie_datasets.md similarity index 60% rename from doc/doc_ch/dataset/docvqa_datasets.md rename to doc/doc_ch/dataset/kie_datasets.md index 3ec1865e..4535ae5f 100644 --- a/doc/doc_ch/dataset/docvqa_datasets.md +++ b/doc/doc_ch/dataset/kie_datasets.md @@ -1,10 +1,14 @@ -## DocVQA数据集 +# 信息抽取数据集 + 这里整理了常见的DocVQA数据集,持续更新中,欢迎各位小伙伴贡献数据集~ - [FUNSD数据集](#funsd) - [XFUND数据集](#xfund) +- [wildreceipt数据集](#wildreceipt) -#### 1、FUNSD数据集 + +## 1. FUNSD数据集 + - **数据来源**:https://guillaumejaume.github.io/FUNSD/ - **数据简介**:FUNSD数据集是一个用于表单理解的数据集,它包含199张真实的、完全标注的扫描版图片,类型包括市场报告、广告以及学术报告等,并分为149张训练集以及50张测试集。FUNSD数据集适用于多种类型的DocVQA任务,如字段级实体分类、字段级实体连接等。部分图像以及标注框可视化如下所示:
@@ -16,12 +20,33 @@ - **下载地址**:https://guillaumejaume.github.io/FUNSD/download/ -#### 2、XFUND数据集 + +## 2. XFUND数据集 - **数据来源**:https://github.com/doc-analysis/XFUND - **数据简介**:XFUND是一个多语种表单理解数据集,它包含7种不同语种的表单数据,并且全部用人工进行了键-值对形式的标注。其中每个语种的数据都包含了199张表单数据,并分为149张训练集以及50张测试集。部分图像以及标注框可视化如下所示: +
- **下载地址**:https://github.com/doc-analysis/XFUND/releases/tag/v1.0 + + + + +## 3. wildreceipt数据集 + +- **数据来源**:https://arxiv.org/abs/2103.14470 +- **数据简介**:wildreceipt数据集是英文发票数据集,包含26个类别(此处类别体系包含`Ignore`类别),共标注了50000个文本框。其中训练集包含1267张图片,测试集包含472张图片。部分图像以及标注框可视化如下所示: + +
+ + +
+ +**注:** 这里对于类别为`Ignore`或者`Others`的文本,没有进行可视化。 + +- **下载地址**: + - 原始数据下载地址:[链接](https://download.openmmlab.com/mmocr/data/wildreceipt.tar) + - 数据格式转换后适配于PaddleOCR训练的数据下载地址:[链接](https://paddleocr.bj.bcebos.com/ppstructure/dataset/wildreceipt.tar) diff --git a/doc/doc_ch/kie.md b/doc/doc_ch/kie.md new file mode 100644 index 00000000..eed0694a --- /dev/null +++ b/doc/doc_ch/kie.md @@ -0,0 +1,463 @@ +# 关键信息抽取 + +本文提供了PaddleOCR关键信息抽取的全流程指南,包括语义实体识别 (Semantic Entity Recognition) 以及关系抽取 (Relation Extraction, RE) 任务的数据准备、模型训练、调优、评估、预测,各个阶段的详细说明。 + +- [1. 数据准备](#1-数据准备) + - [1.1. 准备数据集](#11-准备数据集) + - [1.2. 自定义数据集](#12-自定义数据集) + - [1.3. 数据下载](#13-数据下载) +- [2. 开始训练](#2-开始训练) + - [2.1. 启动训练](#21-启动训练) + - [2.2. 断点训练](#22-断点训练) + - [2.3. 混合精度训练](#24-混合精度训练) + - [2.4. 分布式训练](#25-分布式训练) + - [2.5. 知识蒸馏训练](#26-知识蒸馏训练) + - [2.6. 其他训练环境](#27-其他训练环境) +- [3. 模型评估与预测](#3-模型评估与预测) + - [3.1. 指标评估](#31-指标评估) + - [3.2. 测试信息抽取效果](#32-测试识别效果) +- [4. 模型导出与预测](#4-模型导出与预测) +- [5. FAQ](#5-faq) + +# 1. 数据准备 + +## 1.1. 准备数据集 + +在训练信息抽取相关模型时,PaddleOCR支持以下数据格式。 + - `通用数据` 用于训练以文本文件存储的数据集(SimpleDataSet); + +训练数据的默认存储路径是 `PaddleOCR/train_data`,如果您的磁盘上已有数据集,只需创建软链接至数据集目录: + +``` +# linux and mac os +ln -sf /train_data/dataset +# windows +mklink /d /train_data/dataset +``` + +## 1.2. 自定义数据集 + +训练过程中一般包含训练集与验证集,二者数据格式相同,下面介绍如何自定义数据集。 + +**(1)训练集** + +建议将训练图片放入同一个文件夹,并用一个文本文件记录图片路径和标签,文本文件里的内容如下: + +```py +" 图像文件名 图像标注信息 " +zh_train_0.jpg [{"transcription": "汇丰晋信", "label": "other", "points": [[104, 114], [530, 114], [530, 175], [104, 175]], "id": 1, "linking": []}, {"transcription": "受理时间:", "label": "question", "points": [[126, 267], [266, 267], [266, 305], [126, 305]], "id": 7, "linking": [[7, 13]]}, {"transcription": "2020.6.15", "label": "answer", "points": [[321, 239], [537, 239], [537, 285], [321, 285]], "id": 13, "linking": [[7, 13]]}] +zh_train_1.jpg [{"transcription": "中国人体器官捐献", "label": "other", "points": [[544, 459], [954, 459], [954, 517], [544, 517]], "id": 1, "linking": []}, {"transcription": ">编号:MC545715483585", "label": "other", "points": [[1462, 470], [2054, 470], [2054, 543], [1462, 543]], "id": 10, "linking": []}, {"transcription": "CHINAORGANDONATION", "label": "other", "points": [[543, 516], [958, 516], [958, 551], [543, 551]], "id": 14, "linking": []}, {"transcription": "中国人体器官捐献志愿登记表", "label": "header", "points": [[635, 793], [1892, 793], [1892, 904], [635, 904]], "id": 18, "linking": []}] +... +``` + +**注意:** 文本文件中默认请将图片路径和图片标签用 `\t` 分割,如用其他方式分割将造成训练报错。 + +其中图像标注信息字符串经过json解析之后可以得到一个列表信息,列表中每个元素是一个字典,存储了每个文本行的需要信息,各个字段的含义如下。 + +- transcription: 存储了文本行的文字内容 +- label: 该文本行内容所属的类别 +- points: 存储文本行的四点位置信息 +- id: 存储文本行的id信息,用于RE任务的训练 +- linking: 存储文本行的之间的连接信息,用于RE任务的训练 + +**(2)验证集** + +验证集构建方式与训练集相同。 + +* 字典文件 + +训练集与验证集中的文本行包含标签信息,所有标签的列表存在字典文件中(如`class_list.txt`),字典文件中的每一行表示为一个类别名称。 + +以XFUND_zh数据为例,共包含4个类别,字典文件内容如下所示。 + +``` +OTHER +QUESTION +ANSWER +HEADER +``` + +在标注文件中,每个标注的文本行内容的`label`字段标注信息需要属于字典内容。 + +最终数据集应有如下文件结构: + +``` +|-train_data + |-data_name + |- train.json + |- train + |- zh_train_0.png + |- zh_train_1.jpg + | ... + |- val.json + |- val + |- zh_val_0.png + |- zh_val_1.jpg + | ... +``` + +**注:** + +- 标注文件中的类别信息不区分大小写,如`HEADER`与`header`会被解析为相同的类别id,因此在标注的时候,不能使用小写处理后相同的字符串表示不同的类别。 +- 在整理标注文件的时候,建议将other这个类别(其他,无需关注的文本行可以标注为other)放在第一行,在解析的时候,会将`other`类别的类别id解析为0,后续不会对该类进行可视化。 + +## 1.3. 数据下载 + +如果你没有本地数据集,可以从[XFUND](https://github.com/doc-analysis/XFUND)或者[FUNSD](https://guillaumejaume.github.io/FUNSD/)官网下载数据,然后使用XFUND与FUNSD的处理脚本([XFUND](../../ppstructure/vqa/tools/trans_xfun_data.py), [FUNSD](../../ppstructure/vqa/tools/trans_funsd_label.py)),生成用于PaddleOCR训练的数据格式,并使用公开数据集快速体验关键信息抽取的流程。 + +更多关于公开数据集的介绍,请参考[关键信息抽取数据集说明文档](./dataset/kie_datasets.md)。 + +PaddleOCR也支持了关键信息抽取模型的标注,具体使用方法请参考:[PPOCRLabel使用文档](../../PPOCRLabel/README_ch.md)。 + + +# 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 VI_LayoutXLM 多模态预训练模型为例进行讲解。 + +> 如果希望使用基于SDMGR的关键信息抽取算法,请参考:[SDMGR使用](./algorithm_kie_sdmgr.md)。 + +## 2.1. 启动训练 + +如果你没有使用自定义数据集,可以使用PaddleOCR中已经处理好的XFUND_zh数据集进行快速体验。 + +```bash +mkdir train_data +cd train_data +wget https://paddleocr.bj.bcebos.com/ppstructure/dataset/XFUND.tar && tar -xf XFUND.tar +cd .. +``` + +如果不希望训练,直接体验后面的模型评估、预测、动转静、推理的流程,可以下载PaddleOCR中提供的预训练模型,并跳过2.1部分。 + +使用下面的方法,下载基于XFUND数据的SER与RE任务预训练模型。 + +```bash +mkdir pretrained_model +cd pretrained_model +# 下载并解压SER预训练模型 +wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_pretrained.tar & tar -xf ser_vi_layoutxlm_xfund_pretrained.tar + +# 下载并解压RE预训练模型 +wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_pretrained.tar & tar -xf re_vi_layoutxlm_xfund_pretrained.tar +``` + +开始训练: + +- 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 +- PaddleOCR在训练时,会默认下载VI_LayoutXLM预训练模型,这里无需预先下载。 + +```bash +# GPU训练 支持单卡,多卡训练 +# 训练日志会自动保存到 配置文件中"{Global.save_model_dir}" 下的train.log文件中 + +# SER单卡训练 +python3 tools/train.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml + +# SER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/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml + +# RE任务单卡训练 +python3 tools/train.py -c configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh.yml +``` + +以SER任务为例,正常启动训练后,会看到以下log输出: + +``` +[2022/08/08 16:28:28] ppocr INFO: epoch: [1/200], global_step: 10, lr: 0.000006, loss: 1.871535, avg_reader_cost: 0.28200 s, avg_batch_cost: 0.82318 s, avg_samples: 8.0, ips: 9.71838 samples/s, eta: 0:51:59 +[2022/08/08 16:28:33] ppocr INFO: epoch: [1/200], global_step: 19, lr: 0.000018, loss: 1.461939, avg_reader_cost: 0.00042 s, avg_batch_cost: 0.32037 s, avg_samples: 6.9, ips: 21.53773 samples/s, eta: 0:37:55 +[2022/08/08 16:28:39] ppocr INFO: cur metric, precision: 0.11526348939743859, recall: 0.19776657060518732, hmean: 0.14564265817747712, fps: 34.008392345050055 +[2022/08/08 16:28:45] ppocr INFO: save best model is to ./output/ser_vi_layoutxlm_xfund_zh/best_accuracy +[2022/08/08 16:28:45] ppocr INFO: best metric, hmean: 0.14564265817747712, precision: 0.11526348939743859, recall: 0.19776657060518732, fps: 34.008392345050055, best_epoch: 1 +[2022/08/08 16:28:51] ppocr INFO: save model in ./output/ser_vi_layoutxlm_xfund_zh/latest +``` + +log 中自动打印如下信息: + +| 字段 | 含义 | +| :----: | :------: | +| epoch | 当前迭代轮次 | +| iter | 当前迭代次数 | +| lr | 当前学习率 | +| loss | 当前损失函数 | +| reader_cost | 当前 batch 数据处理耗时 | +| batch_cost | 当前 batch 总耗时 | +| samples | 当前 batch 内的样本数 | +| ips | 每秒处理图片的数量 | + + +PaddleOCR支持训练和评估交替进行, 可以在 `configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml` 中修改 `eval_batch_step` 设置评估频率,默认每19个iter评估一次。评估过程中默认将最佳hmean模型,保存为 `output/ser_vi_layoutxlm_xfund_zh/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/kie/` 路径下的多种模型配置进行训练,PaddleOCR支持的信息抽取算法可以参考[前沿算法列表](./algorithm_overview.md)。 + + +如果你希望训练自己的数据集,需要修改配置文件中的数据配置、字典文件以及类别数。 + + +以 `configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml` 为例,修改的内容如下所示。 + +```yaml + +Architecture: + # ... + Backbone: + name: LayoutXLMForSer + pretrained: True + mode: vi + # 假设字典中包含n个字段(包含other),由于采用BIO标注,则类别数为2n-1 + num_classes: &num_classes 7 + +PostProcess: + name: VQASerTokenLayoutLMPostProcess + # 修改字典文件的路径为你自定义的数据集的字典路径 + class_path: &class_path train_data/XFUND/class_list_xfun.txt + +Train: + dataset: + name: SimpleDataSet + # 修改为你自己的训练数据目录 + data_dir: train_data/XFUND/zh_train/image + # 修改为你自己的训练数据标签文件 + label_file_list: + - train_data/XFUND/zh_train/train.json + ... + loader: + # 训练时的单卡batch_size + batch_size_per_card: 8 + ... + +Eval: + dataset: + name: SimpleDataSet + # 修改为你自己的验证数据目录 + data_dir: train_data/XFUND/zh_val/image + # 修改为你自己的验证数据标签文件 + label_file_list: + - train_data/XFUND/zh_val/val.json + ... + loader: + # 验证时的单卡batch_size + batch_size_per_card: 8 +``` + +**注意,预测/评估时的配置文件请务必与训练一致。** + +## 2.2. 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` Architecture.Backbone.checkpoints`指定要加载的模型路径: + +```bash +python3 tools/train.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./output/ser_vi_layoutxlm_xfund_zh/best_accuracy +``` + +**注意**: + +- `Architecture.Backbone.checkpoints`的优先级高于`Architecture.Backbone.pretrained`,需要加载之前训练好的训练模型进行模型微调、恢复训练、模型评估时,需要使用`Architecture.Backbone.checkpoints`指定模型参数路径;如果需要使用默认提供的通用预训练模型进行训练,则需要指定`Architecture.Backbone.pretrained`为`True`,同时指定`Architecture.Backbone.checkpoints`为空(`null`)。 +- LayoutXLM系列模型均是调用了PaddleNLP中的预训练模型,模型加载与保存的逻辑与PaddleNLP基本一致,因此在这里不需要指定`Global.pretrained_model`或者`Global.checkpoints`参数;此外,LayoutXLM系列模型的蒸馏训练目前不支持断点训练。 + + +## 2.3. 混合精度训练 + +coming soon! + +## 2.4. 分布式训练 + +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/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml +``` + +**注意:** (1)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;(2)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`;(3)更多关于分布式训练的性能优势等信息,请参考:[分布式训练教程](./distributed_training.md)。 + +## 2.5. 知识蒸馏训练 + +PaddleOCR支持了基于U-DML知识蒸馏的关键信息抽取模型训练过程,配置文件请参考:[ser_vi_layoutxlm_xfund_zh_udml.yml](../../configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh_udml.yml),更多关于知识蒸馏的说明文档请参考:[知识蒸馏说明文档](./knowledge_distillation.md)。 + +**注意**: PaddleOCR中LayoutXLM系列关键信息抽取模型的保存与加载逻辑与PaddleNLP保持一致,因此在蒸馏的过程中仅保存了学生模型的参数,如果希望使用保存的模型进行评估,需要使用学生模型的配置(上面的蒸馏文件对应的学生模型为[ser_vi_layoutxlm_xfund_zh.yml](../../configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml)) + + +## 2.6. 其他训练环境 + +- Windows GPU/CPU +在Windows平台上与Linux平台略有不同: +Windows平台只支持`单卡`的训练与预测,指定GPU进行训练`set CUDA_VISIBLE_DEVICES=0` +在Windows平台,DataLoader只支持单进程模式,因此需要设置 `num_workers` 为0; + +- macOS +不支持GPU模式,需要在配置文件中设置`use_gpu`为False,其余训练评估预测命令与Linux GPU完全相同。 + +- Linux DCU +DCU设备上运行需要设置环境变量 `export HIP_VISIBLE_DEVICES=0,1,2,3`,其余训练评估预测命令与Linux GPU完全相同。 + + +# 3. 模型评估与预测 + +## 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`Architecture.Backbone.checkpoints`指向保存的参数文件。评估数据集可以通过 `configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml` 修改Eval中的 `label_file_path` 设置。 + +```bash +# GPU 评估, Global.checkpoints 为待测权重 +python3 tools/eval.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./output/ser_vi_layoutxlm_xfund_zh/best_accuracy +``` + +会输出以下信息,打印出precision、recall、hmean等信息。 + +```py +[2022/08/09 07:59:28] ppocr INFO: metric eval *************** +[2022/08/09 07:59:28] ppocr INFO: precision:0.697476609016161 +[2022/08/09 07:59:28] ppocr INFO: recall:0.8861671469740634 +[2022/08/09 07:59:28] ppocr INFO: hmean:0.7805806758686339 +[2022/08/09 07:59:28] ppocr INFO: fps:17.367364606899105 +``` + + +## 3.2. 测试信息抽取结果 + +使用 PaddleOCR 训练好的模型,可以通过以下脚本进行快速预测。 + +默认预测的图片存储在 `infer_img` 里,通过 `-o Architecture.Backbone.checkpoints` 加载训练好的参数文件: + +根据配置文件中设置的 `save_model_dir` 和 `save_epoch_step` 字段,会有以下几种参数被保存下来: + +``` +output/ser_vi_layoutxlm_xfund_zh/ +├── best_accuracy + ├── metric.states + ├── model_config.json + ├── model_state.pdparams +├── best_accuracy.pdopt +├── config.yml +├── train.log +├── latest + ├── metric.states + ├── model_config.json + ├── model_state.pdparams +├── latest.pdopt +``` + +其中 best_accuracy.* 是评估集上的最优模型;latest.* 是最新保存的一个模型。 + + +预测使用的配置文件必须与训练一致,如您通过 `python3 tools/train.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml` 完成了模型的训练过程。 + +您可以使用如下命令进行中文模型预测。 + + +```bash +python3 tools/infer_vqa_token_ser.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./output/ser_vi_layoutxlm_xfund_zh/best_accuracy Global.infer_img=./ppstructure/docs/vqa/input/zh_val_42.jpg +``` + +预测图片如下所示,图片会存储在`Global.save_res_path`路径中。 + +
+ +
+ +预测过程中,默认会加载PP-OCRv3的检测识别模型,用于OCR的信息抽取,如果希望加载预先获取的OCR结果,可以使用下面的方式进行预测,指定`Global.infer_img`为标注文件,其中包含图片路径以及OCR信息,同时指定`Global.infer_mode`为False,表示此时不使用OCR预测引擎。 + +```bash +python3 tools/infer_vqa_token_ser.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./output/ser_vi_layoutxlm_xfund_zh/best_accuracy Global.infer_img=./train_data/XFUND/zh_val/val.json Global.infer_mode=False +``` + +对于上述图片,如果使用标注的OCR结果进行信息抽取,预测结果如下。 + +
+ +
+ +可以看出,部分检测框信息更加准确,但是整体信息抽取识别结果基本一致。 + + +在RE任务模型预测时,需要先给出模型SER结果,因此需要同时加载SER的配置文件与模型权重,示例如下。 + + +```bash +python3 ./tools/infer_vqa_token_ser_re.py -c configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./pretrain_models/re_vi_layoutxlm_udml_xfund_zh/re_layoutxlm_xfund_zh_v4_udml/best_accuracy/ Global.infer_img=./train_data/XFUND/zh_val/image/ -c_ser configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o_ser Architecture.Backbone.checkpoints=pretrain_models/ser_vi_layoutxlm_udml_xfund_zh/best_accuracy/ +``` + +预测结果如下所示。 + +
+ +
+ + +如果希望使用标注或者预先获取的OCR信息进行关键信息抽取,同上,可以指定`Global.infer_mode`为False,指定`Global.infer_img`为标注文件。 + +```bash +python3 ./tools/infer_vqa_token_ser_re.py -c configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./pretrain_models/re_vi_layoutxlm_udml_xfund_zh/re_layoutxlm_xfund_zh_v4_udml/best_accuracy/ Global.infer_img=./train_data/XFUND/zh_val/val.json Global.infer_mode=False -c_ser configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o_ser Architecture.Backbone.checkpoints=pretrain_models/ser_vi_layoutxlm_udml_xfund_zh/best_accuracy/ +``` + +其中`c_ser`表示SER的配置文件,`o_ser` 后面需要加上待修改的SER模型与配置文件,如预训练权重等。 + + +预测结果如下所示。 + +
+ +
+ +可以看出,直接使用标注的OCR结果的RE预测结果要更加准确一些。 + +# 4. 模型导出与预测 + + +## 4.1 模型导出 + +inference 模型(`paddle.jit.save`保存的模型) +一般是模型训练,把模型结构和模型参数保存在文件中的固化模型,多用于预测部署场景。 +训练过程中保存的模型是checkpoints模型,保存的只有模型的参数,多用于恢复训练等。 +与checkpoints模型相比,inference 模型会额外保存模型的结构信息,在预测部署、加速推理上性能优越,灵活方便,适合于实际系统集成。 + +信息抽取模型中的SER任务转inference模型步骤如下: + +```bash +# -c 后面设置训练算法的yml配置文件 +# -o 配置可选参数 +# Global.pretrained_model 参数设置待转换的训练模型地址。 +# 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/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./output/ser_vi_layoutxlm_xfund_zh/best_accuracy Global.save_inference_dir=./inference/ser_vi_layoutxlm +``` + +转换成功后,在目录下有三个文件: + +``` +inference/ser_vi_layoutxlm/ + ├── inference.pdiparams # inference模型的参数文件 + ├── inference.pdiparams.info # inference模型的参数信息,可忽略 + └── inference.pdmodel # inference模型的模型结构文件 +``` + +RE任务的动转静过程适配中,敬请期待。 + +## 4.2 模型推理 + +VI-LayoutXLM模型基于SER任务进行推理,可以执行如下命令: + +```bash +cd ppstructure +python3 vqa/predict_vqa_token_ser.py \ + --vqa_algorithm=LayoutXLM \ + --ser_model_dir=../inference/ser_vi_layoutxlm \ + --image_dir=./docs/vqa/input/zh_val_42.jpg \ + --ser_dict_path=../train_data/XFUND/class_list_xfun.txt \ + --vis_font_path=../doc/fonts/simfang.ttf \ + --ocr_order_method="tb-yx" +``` + +可视化SER结果结果默认保存到`./output`文件夹里面。结果示例如下: + +
+ +
+ + +# 5. FAQ + +Q1: 训练模型转inference 模型之后预测效果不一致? + +**A**:该问题多是trained model预测时候的预处理、后处理参数和inference model预测的时候的预处理、后处理参数不一致导致的。可以对比训练使用的配置文件中的预处理、后处理和预测时是否存在差异。 diff --git a/ppstructure/docs/kie_en.md b/doc/doc_en/algorithm_sdmgr_en.md similarity index 100% rename from ppstructure/docs/kie_en.md rename to doc/doc_en/algorithm_sdmgr_en.md diff --git a/ppocr/modeling/backbones/vqa_layoutlm.py b/ppocr/modeling/backbones/vqa_layoutlm.py index d4ced350..ec64b032 100644 --- a/ppocr/modeling/backbones/vqa_layoutlm.py +++ b/ppocr/modeling/backbones/vqa_layoutlm.py @@ -113,7 +113,6 @@ class LayoutLMv2ForSer(NLPBaseModel): pretrained, checkpoints, num_classes=num_classes) - self.use_visual_backbone = True if hasattr(self.model.layoutlmv2, "use_visual_backbone" ) and self.model.layoutlmv2.use_visual_backbone is False: self.use_visual_backbone = False @@ -155,7 +154,9 @@ class LayoutXLMForSer(NLPBaseModel): pretrained, checkpoints, num_classes=num_classes) - self.use_visual_backbone = True + if hasattr(self.model.layoutxlm, "use_visual_backbone" + ) and self.model.layoutxlm.use_visual_backbone is False: + self.use_visual_backbone = False def forward(self, x): if self.use_visual_backbone is True: @@ -185,6 +186,9 @@ class LayoutLMv2ForRe(NLPBaseModel): super(LayoutLMv2ForRe, self).__init__( LayoutLMv2Model, LayoutLMv2ForRelationExtraction, mode, "re", pretrained, checkpoints) + if hasattr(self.model.layoutlmv2, "use_visual_backbone" + ) and self.model.layoutlmv2.use_visual_backbone is False: + self.use_visual_backbone = False def forward(self, x): x = self.model( @@ -207,7 +211,6 @@ class LayoutXLMForRe(NLPBaseModel): super(LayoutXLMForRe, self).__init__( LayoutXLMModel, LayoutXLMForRelationExtraction, mode, "re", pretrained, checkpoints) - self.use_visual_backbone = True if hasattr(self.model.layoutxlm, "use_visual_backbone" ) and self.model.layoutxlm.use_visual_backbone is False: self.use_visual_backbone = False diff --git a/ppocr/utils/save_load.py b/ppocr/utils/save_load.py index 7cd205e8..7ccadb00 100644 --- a/ppocr/utils/save_load.py +++ b/ppocr/utils/save_load.py @@ -56,7 +56,7 @@ def load_model(config, model, optimizer=None, model_type='det'): is_float16 = False if model_type == 'vqa': - # NOTE: for vqa model, resume training is not supported now + # NOTE: for vqa model dsitillation, resume training is not supported now if config["Architecture"]["algorithm"] in ["Distillation"]: return best_model_dict checkpoints = config['Architecture']['Backbone']['checkpoints'] @@ -191,8 +191,7 @@ def save_model(model, """ _mkdir_if_not_exist(model_path, logger) model_prefix = os.path.join(model_path, prefix) - if config['Architecture']["model_type"] != 'vqa': - paddle.save(optimizer.state_dict(), model_prefix + '.pdopt') + paddle.save(optimizer.state_dict(), model_prefix + '.pdopt') if config['Architecture']["model_type"] != 'vqa': paddle.save(model.state_dict(), model_prefix + '.pdparams') metric_prefix = model_prefix diff --git a/ppstructure/docs/imgs/0.png b/ppstructure/docs/imgs/sdmgr_result.png similarity index 100% rename from ppstructure/docs/imgs/0.png rename to ppstructure/docs/imgs/sdmgr_result.png diff --git a/ppstructure/docs/kie.md b/ppstructure/docs/kie.md deleted file mode 100644 index 315dd9f7..00000000 --- a/ppstructure/docs/kie.md +++ /dev/null @@ -1,71 +0,0 @@ -- [关键信息提取(Key Information Extraction)](#关键信息提取key-information-extraction) - - [1. 快速使用](#1-快速使用) - - [2. 执行训练](#2-执行训练) - - [3. 执行评估](#3-执行评估) - - [4. 参考文献](#4-参考文献) - -# 关键信息提取(Key Information Extraction) - -本节介绍PaddleOCR中关键信息提取SDMGR方法的快速使用和训练方法。 - -SDMGR是一个关键信息提取算法,将每个检测到的文本区域分类为预定义的类别,如订单ID、发票号码,金额等。 - - -## 1. 快速使用 - -训练和测试的数据采用wildreceipt数据集,通过如下指令下载数据集: - -``` -wget https://paddleocr.bj.bcebos.com/ppstructure/dataset/wildreceipt.tar && tar xf wildreceipt.tar -``` - -执行预测: - -``` -cd PaddleOCR/ -wget https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar && tar xf kie_vgg16.tar -python3.7 tools/infer_kie.py -c configs/kie/kie_unet_sdmgr.yml -o Global.checkpoints=kie_vgg16/best_accuracy Global.infer_img=../wildreceipt/1.txt -``` - -执行预测后的结果保存在`./output/sdmgr_kie/predicts_kie.txt`文件中,可视化结果保存在`/output/sdmgr_kie/kie_results/`目录下。 - -可视化结果如下图所示: - -
- -
- -## 2. 执行训练 - -创建数据集软链到PaddleOCR/train_data目录下: -``` -cd PaddleOCR/ && mkdir train_data && cd train_data - -ln -s ../../wildreceipt ./ -``` - -训练采用的配置文件是configs/kie/kie_unet_sdmgr.yml,配置文件中默认训练数据路径是`train_data/wildreceipt`,准备好数据后,可以通过如下指令执行训练: -``` -python3.7 tools/train.py -c configs/kie/kie_unet_sdmgr.yml -o Global.save_model_dir=./output/kie/ -``` -## 3. 执行评估 - -``` -python3.7 tools/eval.py -c configs/kie/kie_unet_sdmgr.yml -o Global.checkpoints=./output/kie/best_accuracy -``` - - -## 4. 参考文献 - - - -```bibtex -@misc{sun2021spatial, - title={Spatial Dual-Modality Graph Reasoning for Key Information Extraction}, - author={Hongbin Sun and Zhanghui Kuang and Xiaoyu Yue and Chenhao Lin and Wayne Zhang}, - year={2021}, - eprint={2103.14470}, - archivePrefix={arXiv}, - primaryClass={cs.CV} -} -``` diff --git a/ppstructure/docs/vqa/result_re/zh_val_42_re.jpg b/ppstructure/docs/vqa/result_re/zh_val_42_re.jpg new file mode 100644 index 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type=str, default="../train_data/XFUND/class_list_xfun.txt") + # need to be None or tb-yx + parser.add_argument("--ocr_order_method", type=str, default=None) # params for inference parser.add_argument( "--mode", diff --git a/ppstructure/vqa/predict_vqa_token_ser.py b/ppstructure/vqa/predict_vqa_token_ser.py index 3097ebcf..855be42d 100644 --- a/ppstructure/vqa/predict_vqa_token_ser.py +++ b/ppstructure/vqa/predict_vqa_token_ser.py @@ -40,14 +40,16 @@ logger = get_logger() class SerPredictor(object): def __init__(self, args): - self.ocr_engine = PaddleOCR(use_angle_cls=False, show_log=False) + self.ocr_engine = PaddleOCR( + use_angle_cls=False, show_log=False, use_gpu=args.use_gpu) pre_process_list = [{ 'VQATokenLabelEncode': { 'algorithm': args.vqa_algorithm, 'class_path': args.ser_dict_path, 'contains_re': False, - 'ocr_engine': self.ocr_engine + 'ocr_engine': self.ocr_engine, + 'order_method': args.ocr_order_method, } }, { 'VQATokenPad': { diff --git a/tools/export_model.py b/tools/export_model.py index 2443d66c..a61e5ca9 100755 --- a/tools/export_model.py +++ b/tools/export_model.py @@ -122,7 +122,7 @@ def export_single_model(model, paddle.static.InputSpec( shape=[None, 3, 224, 224], dtype="int64"), # image ] - if arch_config["algorithm"] == "LayoutLM": + if model.backbone.use_visual_backbone is False: input_spec.pop(4) model = to_static(model, input_spec=[input_spec]) else: diff --git a/tools/infer/utility.py b/tools/infer/utility.py index 9c89a4e7..55ec0a23 100644 --- a/tools/infer/utility.py +++ b/tools/infer/utility.py @@ -212,18 +212,24 @@ def create_predictor(args, mode, logger): workspace_size=1 << 30, precision_mode=precision, max_batch_size=args.max_batch_size, - min_subgraph_size=args.min_subgraph_size, # skip the minmum trt subgraph + min_subgraph_size=args. + min_subgraph_size, # skip the minmum trt subgraph use_calib_mode=False) - + # collect shape if args.shape_info_filename is not None: if not os.path.exists(args.shape_info_filename): config.collect_shape_range_info(args.shape_info_filename) - logger.info(f"collect dynamic shape info into : {args.shape_info_filename}") + logger.info( + f"collect dynamic shape info into : {args.shape_info_filename}" + ) else: - logger.info(f"dynamic shape info file( {args.shape_info_filename} ) already exists, not need to generate again.") - config.enable_tuned_tensorrt_dynamic_shape(args.shape_info_filename, True) - + logger.info( + f"dynamic shape info file( {args.shape_info_filename} ) already exists, not need to generate again." + ) + config.enable_tuned_tensorrt_dynamic_shape( + args.shape_info_filename, True) + use_dynamic_shape = True if mode == "det": min_input_shape = { diff --git a/tools/infer_vqa_token_ser.py b/tools/infer_vqa_token_ser.py index 0173a554..a15d83b1 100755 --- a/tools/infer_vqa_token_ser.py +++ b/tools/infer_vqa_token_ser.py @@ -104,8 +104,6 @@ class SerPredictor(object): batch = transform(data, self.ops) batch = to_tensor(batch) preds = self.model(batch) - if self.algorithm in ['LayoutLMv2', 'LayoutXLM']: - preds = preds[0] post_result = self.post_process_class( preds, segment_offset_ids=batch[6], ocr_infos=batch[7]) -- GitLab

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算法简介](#1-算法简介) +- [2. 环境配置](#2-环境配置) +- [3. 模型训练、评估、预测](#3-模型训练评估预测) +- [4. 推理部署](#4-推理部署) + - [4.1 Python推理](#41-python推理) + - [4.2 C++推理部署](#42-推理部署) + - [4.3 Serving服务化部署](#43-serving服务化部署) + - [4.4 更多推理部署](#44-更多推理部署) +- [5. FAQ](#5-faq) +- [引用](#引用) + + + + +## 1. 算法简介 + + +论文信息: + +> [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) +> +> Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei +> +> 2021 + +在XFUND_zh数据集上,算法复现效果如下: + +|模型|骨干网络|任务|配置文件|hmean|下载链接| +| --- | --- |--|--- | --- | --- | +|LayoutXLM|LayoutXLM-base|SER |[ser_layoutxlm_xfund_zh.yml](../../configs/kie/layoutlm_series/ser_layoutxlm_xfund_zh.yml)|90.38%|[训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar)/[推理模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh_infer.tar)| +|LayoutXLM|LayoutXLM-base|RE | [re_layoutxlm_xfund_zh.yml](../../configs/kie/layoutlm_series/re_layoutxlm_xfund_zh.yml)|74.83%|[训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar)/[推理模型(coming soon)]()| + + + +## 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。 + + + + +## 3. 模型训练、评估、预测 + +请参考[关键信息抽取教程](./kie.md)。PaddleOCR对代码进行了模块化,训练不同的关键信息抽取模型只需要**更换配置文件**即可。 + + + +## 4. 推理部署 + + + +### 4.1 Python推理 + +**注:** 目前RE任务推理过程仍在适配中,下面以SER任务为例,介绍基于LayoutXLM模型的关键信息抽取过程。 + +首先将训练得到的模型转换成inference model。LayoutXLM模型在XFUND_zh数据集上训练的模型为例([模型下载地址](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar)),可以使用下面的命令进行转换。 + +``` bash +wget https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar +tar -xf ser_LayoutXLM_xfun_zh.tar +python3 tools/export_model.py -c configs/kie/layoutlm_series/ser_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./ser_LayoutXLM_xfun_zh/best_accuracy Global.save_inference_dir=./inference/ser_layoutxlm +``` + +LayoutXLM模型基于SER任务进行推理,可以执行如下命令: + +```bash +cd ppstructure +python3 vqa/predict_vqa_token_ser.py \ + --vqa_algorithm=LayoutXLM \ + --ser_model_dir=../inference/ser_layoutxlm_infer \ + --image_dir=./docs/vqa/input/zh_val_42.jpg \ + --ser_dict_path=../train_data/XFUND/class_list_xfun.txt \ + --vis_font_path=../doc/fonts/simfang.ttf +``` + +SER可视化结果默认保存到`./output`文件夹里面,结果示例如下: + +