From b1ae9136228cbdcbaa0168cbb9b42506cd88baf0 Mon Sep 17 00:00:00 2001 From: WenmuZhou <572459439@qq.com> Date: Mon, 6 Dec 2021 13:01:15 +0000 Subject: [PATCH] add re --- ppstructure/vqa/README.md | 140 +++++++--- ppstructure/vqa/data_collator.py | 39 +++ ppstructure/vqa/images/input/zh_val_21.jpg | Bin 0 -> 1435562 bytes ppstructure/vqa/images/input/zh_val_40.jpg | Bin 0 -> 1180327 bytes .../vqa/images/result_re/zh_val_21_re.jpg | Bin 0 -> 1134100 bytes .../vqa/images/result_re/zh_val_40_re.jpg | Bin 0 -> 1028871 bytes .../vqa/images/result_ser/zh_val_0_ser.jpg | Bin 1271719 -> 1252522 bytes .../vqa/images/result_ser/zh_val_42_ser.jpg | Bin 1710051 -> 1707291 bytes ppstructure/vqa/infer_re.py | 162 +++++++++++ ppstructure/vqa/infer_ser_e2e.py | 114 ++++---- ppstructure/vqa/infer_ser_re_e2e.py | 131 +++++++++ ppstructure/vqa/metric.py | 175 ++++++++++++ ppstructure/vqa/train_re.py | 261 ++++++++++++++++++ ppstructure/vqa/train_ser.py | 49 ++-- ppstructure/vqa/utils.py | 155 ++++++++--- 15 files changed, 1059 insertions(+), 167 deletions(-) create mode 100644 ppstructure/vqa/data_collator.py create mode 100644 ppstructure/vqa/images/input/zh_val_21.jpg create mode 100644 ppstructure/vqa/images/input/zh_val_40.jpg create mode 100644 ppstructure/vqa/images/result_re/zh_val_21_re.jpg create mode 100644 ppstructure/vqa/images/result_re/zh_val_40_re.jpg create mode 100644 ppstructure/vqa/infer_re.py create mode 100644 ppstructure/vqa/infer_ser_re_e2e.py create mode 100644 ppstructure/vqa/metric.py create mode 100644 ppstructure/vqa/train_re.py diff --git a/ppstructure/vqa/README.md b/ppstructure/vqa/README.md index 8d117fde..4475f646 100644 --- a/ppstructure/vqa/README.md +++ b/ppstructure/vqa/README.md @@ -1,42 +1,62 @@ -# 视觉问答(VQA) +# 文档视觉问答(DOC-VQA) -VQA主要特性如下: +DOC-VQA是VQA任务中的一种,DOC-VQA主要针对文本图像的文字内容提出问题。 + +PP-Structure 里的 DOC-VQA算法基于PaddleNLP自然语言处理算法库进行开发。 + +主要特性如下: - 集成[LayoutXLM](https://arxiv.org/pdf/2104.08836.pdf)模型以及PP-OCR预测引擎。 -- 支持基于多模态方法的语义实体识别 (Semantic Entity Recognition, SER) 以及关系抽取 (Relation Extraction, RE) 任务。基于 SER 任务,可以完成对图像中的文本识别与分类;基于 RE 任务,可以完成对图象中的文本内容的关系提取(比如判断问题对) -- 支持SER任务与OCR引擎联合的端到端系统预测与评估。 +- 支持基于多模态方法的语义实体识别 (Semantic Entity Recognition, SER) 以及关系抽取 (Relation Extraction, RE) 任务。基于 SER 任务,可以完成对图像中的文本识别与分类;基于 RE 任务,可以完成对图象中的文本内容的关系提取,如判断问题对 - 支持SER任务和RE任务的自定义训练 +- 支持OCR+SER的端到端系统预测与评估。 +- 支持OCR+SER+RE的端到端系统预测。 本项目是 [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/pdf/2104.08836.pdf) 在 Paddle 2.2上的开源实现, 包含了在 [XFUND数据集](https://github.com/doc-analysis/XFUND) 上的微调代码。 -## 1. 效果演示 +## 1 性能 + +我们在 [XFUN](https://github.com/doc-analysis/XFUND) 评估数据集上对算法进行了评估,性能如下 + +|任务| Hmean| 模型下载地址| +|:---:|:---:| :---:| +|SER|0.9056| [链接](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_ser_pretrained.tar)| +|RE|0.7113| [链接](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_re_pretrained.tar)| + + + +## 2. 效果演示 **注意:** 测试图片来源于XFUN数据集。 -### 1.1 SER +### 2.1 SER -
- -
+![](./images/result_ser/zh_val_0_ser.jpg) | ![](./images/result_ser/zh_val_42_ser.jpg) +---|--- -
- -
+图中不同颜色的框表示不同的类别,对于XFUN数据集,有`QUESTION`, `ANSWER`, `HEADER` 3种类别 -其中不同颜色的框表示不同的类别,对于XFUN数据集,有`QUESTION`, `ANSWER`, `HEADER` 3种类别,在OCR检测框的左上方也标出了对应的类别和OCR识别结果。 +* 深紫色:HEADER +* 浅紫色:QUESTION +* 军绿色:ANSWER +在OCR检测框的左上方也标出了对应的类别和OCR识别结果。 -### 1.2 RE -* Coming soon! +### 2.2 RE +![](./images/result_re/zh_val_21_re.jpg) | ![](./images/result_re/zh_val_40_re.jpg) +---|--- -## 2. 安装 +图中红色框表示问题,蓝色框表示答案,问题和答案之间使用绿色线连接。在OCR检测框的左上方也标出了对应的类别和OCR识别结果。 -### 2.1 安装依赖 + +## 3. 安装 + +### 3.1 安装依赖 - **(1) 安装PaddlePaddle** @@ -53,12 +73,12 @@ python3 -m pip install paddlepaddle==2.2 -i https://mirror.baidu.com/pypi/simple 更多需求,请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。 -### 2.2 安装PaddleOCR(包含 PP-OCR 和 VQA ) +### 3.2 安装PaddleOCR(包含 PP-OCR 和 VQA ) - **(1)pip快速安装PaddleOCR whl包(仅预测)** ```bash -pip install "paddleocr>=2.2" # 推荐使用2.2+版本 +pip install paddleocr ``` - **(2)下载VQA源码(预测+训练)** @@ -85,13 +105,14 @@ pip install -e . - **(4)安装VQA的`requirements`** ```bash +cd ppstructure/vqa pip install -r requirements.txt ``` -## 3. 使用 +## 4. 使用 -### 3.1 数据和预训练模型准备 +### 4.1 数据和预训练模型准备 处理好的XFUN中文数据集下载地址:[https://paddleocr.bj.bcebos.com/dataset/XFUND.tar](https://paddleocr.bj.bcebos.com/dataset/XFUND.tar)。 @@ -104,18 +125,15 @@ wget https://paddleocr.bj.bcebos.com/dataset/XFUND.tar 如果希望转换XFUN中其他语言的数据集,可以参考[XFUN数据转换脚本](helper/trans_xfun_data.py)。 -如果希望直接体验预测过程,可以下载我们提供的SER预训练模型,跳过训练过程,直接预测即可。 - -* SER任务预训练模型下载链接:[链接](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_ser_pretrained.tar) -* RE任务预训练模型下载链接:coming soon! +如果希望直接体验预测过程,可以下载我们提供的预训练模型,跳过训练过程,直接预测即可。 -### 3.2 SER任务 +### 4.2 SER任务 * 启动训练 ```shell -python train_ser.py \ +python3.7 train_ser.py \ --model_name_or_path "layoutxlm-base-uncased" \ --train_data_dir "XFUND/zh_train/image" \ --train_label_path "XFUND/zh_train/xfun_normalize_train.json" \ @@ -131,13 +149,7 @@ python train_ser.py \ --seed 2048 ``` -最终会打印出`precision`, `recall`, `f1`等指标,如下所示。 - -``` -best metrics: {'loss': 1.066644651549203, 'precision': 0.8770182068017863, 'recall': 0.9361936193619362, 'f1': 0.9056402979780063} -``` - -模型和训练日志会保存在`./output/ser/`文件夹中。 +最终会打印出`precision`, `recall`, `f1`等指标,模型和训练日志会保存在`./output/ser/`文件夹中。 * 使用评估集合中提供的OCR识别结果进行预测 @@ -159,21 +171,73 @@ export CUDA_VISIBLE_DEVICES=0 python3.7 infer_ser_e2e.py \ --model_name_or_path "./output/PP-Layout_v1.0_ser_pretrained/" \ --max_seq_length 512 \ - --output_dir "output_res_e2e/" + --output_dir "output_res_e2e/" \ + --infer_imgs "images/input/zh_val_0.jpg" ``` * 对`OCR引擎 + SER`预测系统进行端到端评估 ```shell export CUDA_VISIBLE_DEVICES=0 -python helper/eval_with_label_end2end.py --gt_json_path XFUND/zh_val/xfun_normalize_val.json --pred_json_path output_res/infer_results.txt +python3.7 helper/eval_with_label_end2end.py --gt_json_path XFUND/zh_val/xfun_normalize_val.json --pred_json_path output_res/infer_results.txt ``` -3.3 RE任务 +### 3.3 RE任务 -coming soon! +* 启动训练 +```shell +python3 train_re.py \ + --model_name_or_path "layoutxlm-base-uncased" \ + --train_data_dir "XFUND/zh_train/image" \ + --train_label_path "XFUND/zh_train/xfun_normalize_train.json" \ + --eval_data_dir "XFUND/zh_val/image" \ + --eval_label_path "XFUND/zh_val/xfun_normalize_val.json" \ + --label_map_path 'labels/labels_ser.txt' \ + --num_train_epochs 2 \ + --eval_steps 10 \ + --save_steps 500 \ + --output_dir "output/re/" \ + --learning_rate 5e-5 \ + --warmup_steps 50 \ + --per_gpu_train_batch_size 8 \ + --per_gpu_eval_batch_size 8 \ + --evaluate_during_training \ + --seed 2048 + +``` + +最终会打印出`precision`, `recall`, `f1`等指标,模型和训练日志会保存在`./output/re/`文件夹中。 + +* 使用评估集合中提供的OCR识别结果进行预测 + +```shell +export CUDA_VISIBLE_DEVICES=0 +python3 infer_re.py \ + --model_name_or_path "./PP-Layout_v1.0_re_pretrained/" \ + --max_seq_length 512 \ + --eval_data_dir "XFUND/zh_val/image" \ + --eval_label_path "XFUND/zh_val/xfun_normalize_val.json" \ + --label_map_path 'labels/labels_ser.txt' \ + --output_dir "output_res" \ + --per_gpu_eval_batch_size 1 \ + --seed 2048 +``` + +最终会在`output_res`目录下保存预测结果可视化图像以及预测结果文本文件,文件名为`infer_results.txt`。 + +* 使用`OCR引擎 + SER + RE`串联结果 + +```shell +export CUDA_VISIBLE_DEVICES=0 +# python3.7 infer_ser_re_e2e.py \ + --model_name_or_path "./PP-Layout_v1.0_ser_pretrained/" \ + --re_model_name_or_path "./PP-Layout_v1.0_re_pretrained/" \ + --max_seq_length 512 \ + --output_dir "output_ser_re_e2e_train/" \ + --infer_imgs "images/input/zh_val_21.jpg" +``` ## 参考链接 diff --git a/ppstructure/vqa/data_collator.py b/ppstructure/vqa/data_collator.py new file mode 100644 index 00000000..bb31d198 --- /dev/null +++ b/ppstructure/vqa/data_collator.py @@ -0,0 +1,39 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import paddle +import numbers +import numpy as np + + +class DataCollator: + def __call__(self, batch): + data_dict = {} + to_tensor_keys = [] + for sample in batch: + for k, v in sample.items(): + if k not in data_dict: + data_dict[k] = [] + if isinstance(v, (np.ndarray, paddle.Tensor, numbers.Number)): + if k not in to_tensor_keys: + to_tensor_keys.append(k) + data_dict[k].append(v) + for k in to_tensor_keys: + data_dict[k] = paddle.to_tensor(data_dict[k]) + return data_dict + + +class DataCollatorNoBatch: + def __call__(self, batch): + return batch[0] diff --git a/ppstructure/vqa/images/input/zh_val_21.jpg b/ppstructure/vqa/images/input/zh_val_21.jpg new file mode 100644 index 0000000000000000000000000000000000000000..35b572d7dd6a6b42cf43a8a4b33567c0af527d30 GIT binary patch literal 1435562 zcmeFZ1yohvx;MNLL_s>FL0|(aAl)U+rdztZyFsM8Hr*v10wUerDIFr+N=o`JJbKOz z&b|NdzGr;n`^LDFF`R47p3i*NGk;HPf6n~;0zwlPfrx-$fJ6gB0|Nb=2MPXqyDRyh zrn^D@r+xq4?`Jy*{W0=Kgb%PVWS|G=FtF$_KRZFhAQ%t~>_ZsPKN7+N*vBw%4<8}I z1NFFn>;I2h5adKNAO>c2cwCd>0ibnrS>VE|l z|I!%$e6sb)0=7(YGkHN!T02ZqHgz%A32jQ#svq$g+1nyT&zkGk>v9g%^8JNFTT6 zP$f@ZB?bN9$s;rFjxxK4f7!6(&=EwKK%0h+rl zjbx!AL4$*g%J}Y>EGTZQc@Jp9KF*vgwOhMr#YaxmFrcGs?kBz-Y1N{cfeXT~iv2z?OCC+bVyF396x+U<$% z8wg7|PeLQ&NT&~RWGihB={rWU5x3H}IG$xhL*IVv_aZTPwutlSSw22`q~<=$i;Lyl z%%ip)dHA*5!N1OSh+8F-cSNII`1BWP$0^ZZO72Dvo-PQh@@;gACa?Y4Eve5P10ibF 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