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PaddleOCR
提交 86b90aa9 编写于 作者: qq_25193841's avatar qq_25193841
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README.md
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......@@ -13,7 +13,6 @@ English | [简体中文](README_ch.md)
<a href=""><img src="https://img.shields.io/badge/python-3.7+-aff.svg"></a>
<a href=""><img src="https://img.shields.io/badge/os-linux%2C%20win%2C%20mac-pink.svg"></a>
<a href=""><img src="https://img.shields.io/pypi/format/PaddleOCR?color=c77"></a>
<a href="https://github.com/PaddlePaddle/PaddleOCR/graphs/contributors"><img src="https://img.shields.io/github/contributors/PaddlePaddle/PaddleOCR?color=9ea"></a>
<a href="https://pypi.org/project/PaddleOCR/"><img src="https://img.shields.io/pypi/dm/PaddleOCR?color=9cf"></a>
<a href="https://github.com/PaddlePaddle/PaddleOCR/stargazers"><img src="https://img.shields.io/github/stars/PaddlePaddle/PaddleOCR?color=ccf"></a>
</p>
......@@ -24,7 +23,8 @@ PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools
**Recent updates**
- 2021.12.21 OCR open source online course starts. The lesson starts at 8:30 every night and lasts for ten days. Free registration: https://aistudio.baidu.com/aistudio/course/introduce/25207
- 2021.12.21 release PaddleOCR v2.4, release 1 text detection algorithm (PSENet), 3 text recognition algorithms (NRTR、SEED、SAR), 1 key information extraction algorithm (SDMGR) and 3 DocVQA algorithms (LayoutLM、LayoutLMv2，LayoutXLM).
- PaddleOCR R&D team would like to share the key points of PP-OCRv2, at 20:15 pm on September 8th, [Course Address](https://aistudio.baidu.com/aistudio/education/group/info/6758).
- 2021.9.7 release PaddleOCR v2.3, [PP-OCRv2](#PP-OCRv2) is proposed. The inference speed of PP-OCRv2 is 220% higher than that of PP-OCR server in CPU device. The F-score of PP-OCRv2 is 7% higher than that of PP-OCR mobile.
- 2021.8.3 released PaddleOCR v2.2, add a new structured documents analysis toolkit, i.e., [PP-Structure](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/ppstructure/README.md), support layout analysis and table recognition (One-key to export chart images to Excel files).
......@@ -38,7 +38,11 @@ PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools
- Ultra lightweight PP-OCR mobile series models: detection (3.0M) + direction classifier (1.4M) + recognition (5.0M) = 9.4M
- General PP-OCR server series models: detection (47.1M) + direction classifier (1.4M) + recognition (94.9M) = 143.4M
- Support Chinese, English, and digit recognition, vertical text recognition, and long text recognition
- Support multi-language recognition: Korean, Japanese, German, French
- Support multi-language recognition: about 80 languages like Korean, Japanese, German, French, etc
- document structurize system PP-Structure
- support layout analysis and table recognition (support export to Excel)
- support key information extraction
- support DocVQA
- Rich toolkits related to the OCR areas
- Semi-automatic data annotation tool, i.e., PPOCRLabel: support fast and efficient data annotation
- Data synthesis tool, i.e., Style-Text: easy to synthesize a large number of images which are similar to the target scene image
......
README_ch.md
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......@@ -9,7 +9,6 @@
<a href=""><img src="https://img.shields.io/badge/python-3.7+-aff.svg"></a>
<a href=""><img src="https://img.shields.io/badge/os-linux%2C%20win%2C%20mac-pink.svg"></a>
<a href=""><img src="https://img.shields.io/pypi/format/PaddleOCR?color=c77"></a>
<a href="https://github.com/PaddlePaddle/PaddleOCR/graphs/contributors"><img src="https://img.shields.io/github/contributors/PaddlePaddle/PaddleOCR?color=9ea"></a>
<a href="https://pypi.org/project/PaddleOCR/"><img src="https://img.shields.io/pypi/dm/PaddleOCR?color=9cf"></a>
<a href="https://github.com/PaddlePaddle/PaddleOCR/stargazers"><img src="https://img.shields.io/github/stars/PaddlePaddle/PaddleOCR?color=ccf"></a>
</p>
......@@ -20,11 +19,13 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
## 近期更新
- 2021.12.21 《OCR十讲》课程开讲，12月21日起每晚八点半线上授课！ 【免费】报名地址：https://aistudio.baidu.com/aistudio/course/introduce/25207
- 2021.12.21 发布PaddleOCR v2.4。OCR算法新增1种文本检测算法（PSENet），3种文本识别算法（NRTR、SEED、SAR）；文档结构化算法新增1种关键信息提取算法（SDMGR），3种DocVQA算法（LayoutLM、LayoutLMv2，LayoutXLM）。
- PaddleOCR研发团队对最新发版内容技术深入解读，9月8日晚上20:15，[课程回放](https://aistudio.baidu.com/aistudio/education/group/info/6758)。
- 2021.9.7 发布PaddleOCR v2.3与[PP-OCRv2](#PP-OCRv2)，CPU推理速度相比于PP-OCR server提升220%；效果相比于PP-OCR mobile 提升7%。
- 2021.8.3 发布PaddleOCR v2.2，新增文档结构分析[PP-Structure](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/ppstructure/README_ch.md)工具包，支持版面分析与表格识别（含Excel导出）。
> 完整PaddleOCR更新时间线可参考[文档](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/update.md)。
> [更多](./doc/doc_ch/update.md)
## 特性
......@@ -33,11 +34,14 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
- 超轻量PP-OCR mobile移动端系列：检测（3.0M）+方向分类器（1.4M）+ 识别（5.0M）= 9.4M
- 通用PPOCR server系列：检测（47.1M）+方向分类器（1.4M）+ 识别（94.9M）= 143.4M
- 支持中英文数字组合识别、竖排文本识别、长文本识别
- 支持多语言识别：韩语、日语、德语、法语等
- 支持多语言识别：韩语、日语、德语、法语等约80种语言
- PP-Structure文档结构化系统
- 支持版面分析与表格识别（含Excel导出）
- 支持关键信息提取任务
- 支持DocVQA任务
- 丰富易用的OCR相关工具组件
- 半自动数据标注工具PPOCRLabel：支持快速高效的数据标注
- 数据合成工具Style-Text：批量合成大量与目标场景类似的图像
- 文档分析能力PP-Structure：支持版面分析与表格识别（含Excel导出）
- 支持用户自定义训练，提供丰富的预测推理部署方案
- 支持PIP快速安装使用
- 可运行于Linux、Windows、MacOS等多种系统
......@@ -56,6 +60,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
<div align="center">
<img src="https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/dygraph/doc/joinus.PNG" width = "200" height = "200" />
</div>
## 零代码体验
- 在线网站体验：超轻量PP-OCR mobile模型体验地址：https://www.paddlepaddle.org.cn/hub/scene/ocr
......
configs/kie/kie_unet_sdmgr.yml 0 → 100644
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Global:
use_gpu: True
epoch_num: 60
log_smooth_window: 20
print_batch_step: 50
save_model_dir: ./output/kie_5/
save_epoch_step: 50
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [ 0, 80 ]
# 1. If pretrained_model is saved in static mode, such as classification pretrained model
# from static branch, load_static_weights must be set as True.
# 2. If you want to finetune the pretrained models we provide in the docs,
# you should set load_static_weights as False.
load_static_weights: False
cal_metric_during_train: False
pretrained_model:
checkpoints:
save_inference_dir:
use_visualdl: False
class_path: ./train_data/wildreceipt/class_list.txt
infer_img: ./train_data/wildreceipt/1.txt
save_res_path: ./output/sdmgr_kie/predicts_kie.txt
img_scale: [ 1024, 512 ]
Architecture:
model_type: kie
algorithm: SDMGR
Transform:
Backbone:
name: Kie_backbone
Head:
name: SDMGRHead
Loss:
name: SDMGRLoss
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: Piecewise
learning_rate: 0.001
decay_epochs: [ 60, 80, 100]
values: [ 0.001, 0.0001, 0.00001]
warmup_epoch: 2
regularizer:
name: 'L2'
factor: 0.00005
PostProcess:
name: None
Metric:
name: KIEMetric
main_indicator: hmean
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/wildreceipt/
label_file_list: [ './train_data/wildreceipt/wildreceipt_train.txt' ]
ratio_list: [ 1.0 ]
transforms:
- DecodeImage: # load image
img_mode: RGB
channel_first: False
- NormalizeImage:
scale: 1
mean: [ 123.675, 116.28, 103.53 ]
std: [ 58.395, 57.12, 57.375 ]
order: 'hwc'
- KieLabelEncode: # Class handling label
character_dict_path: ./train_data/wildreceipt/dict.txt
- KieResize:
- ToCHWImage:
- KeepKeys:
keep_keys: [ 'image', 'relations', 'texts', 'points', 'labels', 'tag', 'shape'] # dataloader will return list in this order
loader:
shuffle: True
drop_last: False
batch_size_per_card: 4
num_workers: 4
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/wildreceipt
label_file_list:
- ./train_data/wildreceipt/wildreceipt_test.txt
# - /paddle/data/PaddleOCR/train_data/wildreceipt/1.txt
transforms:
- DecodeImage: # load image
img_mode: RGB
channel_first: False
- KieLabelEncode: # Class handling label
character_dict_path: ./train_data/wildreceipt/dict.txt
- KieResize:
- NormalizeImage:
scale: 1
mean: [ 123.675, 116.28, 103.53 ]
std: [ 58.395, 57.12, 57.375 ]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: [ 'image', 'relations', 'texts', 'points', 'labels', 'tag', 'ori_image', 'ori_boxes', 'shape']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 1 # must be 1
num_workers: 4
configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml
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......@@ -28,6 +28,7 @@ Optimizer:
lr:
name: Cosine
learning_rate: 0.001
warmup_epoch: 5
regularizer:
name: 'L2'
factor: 0.00004
......
configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml
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......@@ -28,6 +28,7 @@ Optimizer:
lr:
name: Cosine
learning_rate: 0.001
warmup_epoch: 5
regularizer:
name: 'L2'
factor: 0.00001
......
configs/rec/rec_resnet_stn_bilstm_att.yml
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......@@ -75,7 +75,7 @@ Train:
channel_first: False
- SEEDLabelEncode: # Class handling label
- RecResizeImg:
character_type: en
character_dict_path:
image_shape: [3, 64, 256]
padding: False
- KeepKeys:
......@@ -96,7 +96,7 @@ Eval:
channel_first: False
- SEEDLabelEncode: # Class handling label
- RecResizeImg:
character_type: en
character_dict_path:
image_shape: [3, 64, 256]
padding: False
- KeepKeys:
......
deploy/cpp_infer/readme.md
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......@@ -103,7 +103,7 @@ opencv3/
#### 1.2.1 直接下载安装
* [Paddle预测库官网](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.0/guides/05_inference_deployment/inference/build_and_install_lib_cn.html) 上提供了不同cuda版本的Linux预测库，可以在官网查看并选择合适的预测库版本（*建议选择paddle版本>=2.0.1版本的预测库* ）。
* [Paddle预测库官网](https://paddle-inference.readthedocs.io/en/latest/user_guides/download_lib.html) 上提供了不同cuda版本的Linux预测库，可以在官网查看并选择合适的预测库版本（*建议选择paddle版本>=2.0.1版本的预测库* ）。
* 下载之后使用下面的方法解压。
......@@ -119,7 +119,7 @@ tar -xf paddle_inference.tgz
```shell
git clone https://github.com/PaddlePaddle/Paddle.git
git checkout release/2.1
git checkout develop
```
* 进入Paddle目录后，编译方法如下。
......
deploy/cpp_infer/readme_en.md
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......@@ -79,7 +79,7 @@ opencv3/
#### 1.2.1 Direct download and installation
[Paddle inference library official website](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.0/guides/05_inference_deployment/inference/build_and_install_lib_cn.html). You can view and select the appropriate version of the inference library on the official website.
[Paddle inference library official website](https://paddle-inference.readthedocs.io/en/latest/user_guides/download_lib.html). You can view and select the appropriate version of the inference library on the official website.
* After downloading, use the following method to uncompress.
......@@ -97,7 +97,7 @@ Finally you can see the following files in the folder of `paddle_inference/`.
```shell
git clone https://github.com/PaddlePaddle/Paddle.git
git checkout release/2.1
git checkout develop
```
* After entering the Paddle directory, the commands to compile the paddle inference library are as follows.
......
deploy/pdserving/README.md
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......@@ -45,63 +45,67 @@ PaddleOCR operating environment and Paddle Serving operating environment are nee
```
3. Install the client to send requests to the service
In [download link](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md) find the client installation package corresponding to the python version.
The python3.7 version is recommended here:
```
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.0.0-cp37-none-any.whl
pip3 install paddle_serving_client-0.0.0-cp37-none-any.whl
```
4. Install serving-app
```
pip3 install paddle-serving-app==0.6.1
```
```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
# 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
# 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
# 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
```
**note:** If you want to install the latest version of PaddleServing, refer to [link](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md).
**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).
<a name="model-conversion"></a>
## Model conversion
When using PaddleServing for service deployment, you need to convert the saved inference model into a serving model that is easy to deploy.
Firstly, download the [inference model](https://github.com/PaddlePaddle/PaddleOCR#pp-ocr-20-series-model-listupdate-on-dec-15) of PPOCR
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/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar
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
# Download and unzip the OCR text recognition model
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
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
```
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_ppocr_mobile_v2.0_det_infer/ \
python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_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 ./ppocrv2_det_serving/ \
--serving_client ./ppocrv2_det_client/
# Recognition model conversion
python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_mobile_v2.0_rec_infer/ \
python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_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 ./ppocrv2_rec_serving/ \
--serving_client ./ppocrv2_rec_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:
```
|- ppocr_det_mobile_2.0_serving/
|- __model__
|- __params__
|- serving_server_conf.prototxt
|- serving_server_conf.stream.prototxt
|- ppocr_det_mobile_2.0_client
|- serving_client_conf.prototxt
|- serving_client_conf.stream.prototxt
|- ppocrv2_det_serving/
|- __model__
|- __params__
|- serving_server_conf.prototxt
|- serving_server_conf.stream.prototxt
|- ppocrv2_det_client
|- serving_client_conf.prototxt
|- serving_client_conf.stream.prototxt
```
The recognition model is the same.
......
deploy/pdserving/README_CN.md
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......@@ -34,70 +34,66 @@ PaddleOCR提供2种服务部署方式：
- 准备PaddleServing的运行环境，步骤如下
1. 安装serving，用于启动服务
```
pip3 install paddle-serving-server==0.6.1 # for CPU
pip3 install paddle-serving-server-gpu==0.6.1 # for GPU
# 其他GPU环境需要确认环境再选择执行如下命令
pip3 install paddle-serving-server-gpu==0.6.1.post101 # GPU with CUDA10.1 + TensorRT6
pip3 install paddle-serving-server-gpu==0.6.1.post11 # GPU with CUDA11 + TensorRT7
```
2. 安装client，用于向服务发送请求
在[下载链接](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md)中找到对应python版本的client安装包，这里推荐python3.7版本：
```
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.0.0-cp37-none-any.whl
pip3 install paddle_serving_client-0.0.0-cp37-none-any.whl
```
3. 安装serving-app
```
pip3 install paddle-serving-app==0.6.1
```
```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
# 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
# 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
# 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
```
**Note:** 如果要安装最新版本的PaddleServing参考[链接](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md)。
**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)。
<a name="模型转换"></a>
## 模型转换
使用PaddleServing做服务化部署时，需要将保存的inference模型转换为serving易于部署的模型。
首先，下载PPOCR的[inference模型](https://github.com/PaddlePaddle/PaddleOCR#pp-ocr-20-series-model-listupdate-on-dec-15)
```
首先，下载PPOCR的[inference模型](https://github.com/PaddlePaddle/PaddleOCR#pp-ocr-series-model-listupdate-on-september-8th)
```bash
# 下载并解压 OCR 文本检测模型
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar
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
# 下载并解压 OCR 文本识别模型
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
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
```
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_ppocr_mobile_v2.0_det_infer/ \
python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_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 ./ppocrv2_det_serving/ \
--serving_client ./ppocrv2_det_client/
# 转换识别模型
python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_mobile_v2.0_rec_infer/ \
python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_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 ./ppocrv2_rec_serving/ \
--serving_client ./ppocrv2_rec_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` 和`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`ppocrv2_det_serving` 和`ppocrv2_det_client`的文件夹，具备如下格式：
```
|- ppocr_det_mobile_2.0_serving/
|- ppocrv2_det_serving/
|- __model__
|- __params__
|- serving_server_conf.prototxt
|- serving_server_conf.stream.prototxt
|- ppocr_det_mobile_2.0_client
|- ppocrv2_det_client
|- serving_client_conf.prototxt
|- serving_client_conf.stream.prototxt
......
deploy/pdserving/config.yml
浏览文件 @ 86b90aa9
......@@ -34,7 +34,7 @@ op:
client_type: local_predictor
#det模型路径
model_config: ./ppocr_det_mobile_2.0_serving
model_config: ./ppocrv2_det_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"]
......@@ -60,7 +60,7 @@ op:
client_type: local_predictor
#rec模型路径
model_config: ./ppocr_rec_mobile_2.0_serving
model_config: ./ppocrv2_rec_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"]
......
deploy/pdserving/web_service.py
浏览文件 @ 86b90aa9
......@@ -54,7 +54,7 @@ class DetOp(Op):
_, self.new_h, self.new_w = det_img.shape
return {"x": det_img[np.newaxis, :].copy()}, False, None, ""
def postprocess(self, input_dicts, fetch_dict, log_id):
def postprocess(self, input_dicts, fetch_dict, data_id, log_id):
det_out = fetch_dict["save_infer_model/scale_0.tmp_1"]
ratio_list = [
float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w
......@@ -129,7 +129,7 @@ class RecOp(Op):
return feed_list, False, None, ""
def postprocess(self, input_dicts, fetch_data, log_id):
def postprocess(self, input_dicts, fetch_data, data_id, log_id):
res_list = []
if isinstance(fetch_data, dict):
if len(fetch_data) > 0:
......
deploy/pdserving/web_service_det.py
浏览文件 @ 86b90aa9
......@@ -54,7 +54,7 @@ class DetOp(Op):
_, self.new_h, self.new_w = det_img.shape
return {"x": det_img[np.newaxis, :].copy()}, False, None, ""
def postprocess(self, input_dicts, fetch_dict, log_id):
def postprocess(self, input_dicts, fetch_dict, data_id, log_id):
det_out = fetch_dict["save_infer_model/scale_0.tmp_1"]
ratio_list = [
float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w
......
deploy/pdserving/web_service_rec.py
浏览文件 @ 86b90aa9
......@@ -56,7 +56,7 @@ class RecOp(Op):
feed_list.append(feed)
return feed_list, False, None, ""
def postprocess(self, input_dicts, fetch_data, log_id):
def postprocess(self, input_dicts, fetch_data, data_id, log_id):
res_list = []
if isinstance(fetch_data, dict):
if len(fetch_data) > 0:
......
doc/doc_ch/update.md
浏览文件 @ 86b90aa9
# 更新
- 2021.12.21 《OCR十讲》课程开讲，12月21日起每晚八点半线上授课！ 【免费】报名地址：https://aistudio.baidu.com/aistudio/course/introduce/25207
- 2021.12.21 发布PaddleOCR v2.4。OCR算法新增1种文本检测算法（PSENet），3种文本识别算法（NRTR、SEED、SAR）；文档结构化算法新增1种关键信息提取算法（SDMGR），3种DocVQA算法（LayoutLM、LayoutLMv2，LayoutXLM）。
- 2021.9.7 发布PaddleOCR v2.3，发布[PP-OCRv2](#PP-OCRv2)，CPU推理速度相比于PP-OCR server提升220%；效果相比于PP-OCR mobile 提升7%。
- 2021.8.3 发布PaddleOCR v2.2，新增文档结构分析[PP-Structure](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/ppstructure/README_ch.md)工具包，支持版面分析与表格识别（含Excel导出）。
- 2021.6.29 [FAQ](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/FAQ.md)新增5个高频问题，总数248个，每周一都会更新，欢迎大家持续关注。
......
doc/doc_en/update_en.md
浏览文件 @ 86b90aa9
# RECENT UPDATES
- 2021.12.21 OCR open source online course starts. The lesson starts at 8:30 every night and lasts for ten days. Free registration: https://aistudio.baidu.com/aistudio/course/introduce/25207
- 2021.12.21 release PaddleOCR v2.4, release 1 text detection algorithm (PSENet), 3 text recognition algorithms (NRTR、SEED、SAR), 1 key information extraction algorithm (SDMGR) and 3 DocVQA algorithms (LayoutLM、LayoutLMv2，LayoutXLM).
- 2021.9.7 release PaddleOCR v2.3, [PP-OCRv2](#PP-OCRv2) is proposed. The CPU inference speed of PP-OCRv2 is 220% higher than that of PP-OCR server. The F-score of PP-OCRv2 is 7% higher than that of PP-OCR mobile.
- 2021.8.3 released PaddleOCR v2.2, add a new structured documents analysis toolkit, i.e., [PP-Structure](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/ppstructure/README.md), support layout analysis and table recognition (One-key to export chart images to Excel files).
- 2021.4.8 release end-to-end text recognition algorithm [PGNet](https://www.aaai.org/AAAI21Papers/AAAI-2885.WangP.pdf) which is published in AAAI 2021. Find tutorial [here](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/pgnet_en.md)；release multi language recognition [models](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md), support more than 80 languages recognition; especically, the performance of [English recognition model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/models_list_en.md#English) is Optimized.
......
ppocr/data/imaug/label_ops.py
浏览文件 @ 86b90aa9
......@@ -19,6 +19,7 @@ from __future__ import unicode_literals
import numpy as np
import string
from shapely.geometry import LineString, Point, Polygon
import json
from ppocr.utils.logging import get_logger
......@@ -286,6 +287,168 @@ class E2ELabelEncodeTrain(object):
return data
class KieLabelEncode(object):
def __init__(self, character_dict_path, norm=10, directed=False, **kwargs):
super(KieLabelEncode, self).__init__()
self.dict = dict({'': 0})
with open(character_dict_path, 'r', encoding='utf-8') as fr:
idx = 1
for line in fr:
char = line.strip()
self.dict[char] = idx
idx += 1
self.norm = norm
self.directed = directed
def compute_relation(self, boxes):
"""Compute relation between every two boxes."""
x1s, y1s = boxes[:, 0:1], boxes[:, 1:2]
x2s, y2s = boxes[:, 4:5], boxes[:, 5:6]
ws, hs = x2s - x1s + 1, np.maximum(y2s - y1s + 1, 1)
dxs = (x1s[:, 0][None] - x1s) / self.norm
dys = (y1s[:, 0][None] - y1s) / self.norm
xhhs, xwhs = hs[:, 0][None] / hs, ws[:, 0][None] / hs
whs = ws / hs + np.zeros_like(xhhs)
relations = np.stack([dxs, dys, whs, xhhs, xwhs], -1)
bboxes = np.concatenate([x1s, y1s, x2s, y2s], -1).astype(np.float32)
return relations, bboxes
def pad_text_indices(self, text_inds):
"""Pad text index to same length."""
max_len = 300
recoder_len = max([len(text_ind) for text_ind in text_inds])
padded_text_inds = -np.ones((len(text_inds), max_len), np.int32)
for idx, text_ind in enumerate(text_inds):
padded_text_inds[idx, :len(text_ind)] = np.array(text_ind)
return padded_text_inds, recoder_len
def list_to_numpy(self, ann_infos):
"""Convert bboxes, relations, texts and labels to ndarray."""
boxes, text_inds = ann_infos['points'], ann_infos['text_inds']
boxes = np.array(boxes, np.int32)
relations, bboxes = self.compute_relation(boxes)
labels = ann_infos.get('labels', None)
if labels is not None:
labels = np.array(labels, np.int32)
edges = ann_infos.get('edges', None)
if edges is not None:
labels = labels[:, None]
edges = np.array(edges)
edges = (edges[:, None] == edges[None, :]).astype(np.int32)
if self.directed:
edges = (edges & labels == 1).astype(np.int32)
np.fill_diagonal(edges, -1)
labels = np.concatenate([labels, edges], -1)
padded_text_inds, recoder_len = self.pad_text_indices(text_inds)
max_num = 300
temp_bboxes = np.zeros([max_num, 4])
h, _ = bboxes.shape
temp_bboxes[:h, :h] = bboxes
temp_relations = np.zeros([max_num, max_num, 5])
temp_relations[:h, :h, :] = relations
temp_padded_text_inds = np.zeros([max_num, max_num])
temp_padded_text_inds[:h, :] = padded_text_inds
temp_labels = np.zeros([max_num, max_num])
temp_labels[:h, :h + 1] = labels
tag = np.array([h, recoder_len])
return dict(
image=ann_infos['image'],
points=temp_bboxes,
relations=temp_relations,
texts=temp_padded_text_inds,
labels=temp_labels,
tag=tag)
def convert_canonical(self, points_x, points_y):
assert len(points_x) == 4
assert len(points_y) == 4
points = [Point(points_x[i], points_y[i]) for i in range(4)]
polygon = Polygon([(p.x, p.y) for p in points])
min_x, min_y, _, _ = polygon.bounds
points_to_lefttop = [
LineString([points[i], Point(min_x, min_y)]) for i in range(4)
]
distances = np.array([line.length for line in points_to_lefttop])
sort_dist_idx = np.argsort(distances)
lefttop_idx = sort_dist_idx[0]
if lefttop_idx == 0:
point_orders = [0, 1, 2, 3]
elif lefttop_idx == 1:
point_orders = [1, 2, 3, 0]
elif lefttop_idx == 2:
point_orders = [2, 3, 0, 1]
else:
point_orders = [3, 0, 1, 2]
sorted_points_x = [points_x[i] for i in point_orders]
sorted_points_y = [points_y[j] for j in point_orders]
return sorted_points_x, sorted_points_y
def sort_vertex(self, points_x, points_y):
assert len(points_x) == 4
assert len(points_y) == 4
x = np.array(points_x)
y = np.array(points_y)
center_x = np.sum(x) * 0.25
center_y = np.sum(y) * 0.25
x_arr = np.array(x - center_x)
y_arr = np.array(y - center_y)
angle = np.arctan2(y_arr, x_arr) * 180.0 / np.pi
sort_idx = np.argsort(angle)
sorted_points_x, sorted_points_y = [], []
for i in range(4):
sorted_points_x.append(points_x[sort_idx[i]])
sorted_points_y.append(points_y[sort_idx[i]])
return self.convert_canonical(sorted_points_x, sorted_points_y)
def __call__(self, data):
import json
label = data['label']
annotations = json.loads(label)
boxes, texts, text_inds, labels, edges = [], [], [], [], []
for ann in annotations:
box = ann['points']
x_list = [box[i][0] for i in range(4)]
y_list = [box[i][1] for i in range(4)]
sorted_x_list, sorted_y_list = self.sort_vertex(x_list, y_list)
sorted_box = []
for x, y in zip(sorted_x_list, sorted_y_list):
sorted_box.append(x)
sorted_box.append(y)
boxes.append(sorted_box)
text = ann['transcription']
texts.append(ann['transcription'])
text_ind = [self.dict[c] for c in text if c in self.dict]
text_inds.append(text_ind)
labels.append(ann['label'])
edges.append(ann.get('edge', 0))
ann_infos = dict(
image=data['image'],
points=boxes,
texts=texts,
text_inds=text_inds,
edges=edges,
labels=labels)
return self.list_to_numpy(ann_infos)
class AttnLabelEncode(BaseRecLabelEncode):
""" Convert between text-label and text-index """
......@@ -344,8 +507,12 @@ class SEEDLabelEncode(BaseRecLabelEncode):
max_text_length, character_dict_path, use_space_char)
def add_special_char(self, dict_character):
self.padding = "padding"
self.end_str = "eos"
dict_character = dict_character + [self.end_str]
self.unknown = "unknown"
dict_character = dict_character + [
self.end_str, self.padding, self.unknown
]
return dict_character
def __call__(self, data):
......@@ -356,8 +523,8 @@ class SEEDLabelEncode(BaseRecLabelEncode):
if len(text) >= self.max_text_len:
return None
data['length'] = np.array(len(text)) + 1 # conclude eos
text = text + [len(self.character) - 1] * (self.max_text_len - len(text)
)
text = text + [len(self.character) - 3] + [len(self.character) - 2] * (
self.max_text_len - len(text) - 1)
data['label'] = np.array(text)
return data
......
ppocr/data/imaug/operators.py
浏览文件 @ 86b90aa9
......@@ -111,7 +111,6 @@ class NormalizeImage(object):
from PIL import Image
if isinstance(img, Image.Image):
img = np.array(img)
assert isinstance(img,
np.ndarray), "invalid input 'img' in NormalizeImage"
data['image'] = (
......@@ -367,3 +366,53 @@ class E2EResizeForTest(object):
ratio_w = resize_w / float(w)
return im, (ratio_h, ratio_w)
class KieResize(object):
def __init__(self, **kwargs):
super(KieResize, self).__init__()
self.max_side, self.min_side = kwargs['img_scale'][0], kwargs[
'img_scale'][1]
def __call__(self, data):
img = data['image']
points = data['points']
src_h, src_w, _ = img.shape
im_resized, scale_factor, [ratio_h, ratio_w
], [new_h, new_w] = self.resize_image(img)
resize_points = self.resize_boxes(img, points, scale_factor)
data['ori_image'] = img
data['ori_boxes'] = points
data['points'] = resize_points
data['image'] = im_resized
data['shape'] = np.array([new_h, new_w])
return data
def resize_image(self, img):
norm_img = np.zeros([1024, 1024, 3], dtype='float32')
scale = [512, 1024]
h, w = img.shape[:2]
max_long_edge = max(scale)
max_short_edge = min(scale)
scale_factor = min(max_long_edge / max(h, w),
max_short_edge / min(h, w))
resize_w, resize_h = int(w * float(scale_factor) + 0.5), int(h * float(
scale_factor) + 0.5)
max_stride = 32
resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
im = cv2.resize(img, (resize_w, resize_h))
new_h, new_w = im.shape[:2]
w_scale = new_w / w
h_scale = new_h / h
scale_factor = np.array(
[w_scale, h_scale, w_scale, h_scale], dtype=np.float32)
norm_img[:new_h, :new_w, :] = im
return norm_img, scale_factor, [h_scale, w_scale], [new_h, new_w]
def resize_boxes(self, im, points, scale_factor):
points = points * scale_factor
img_shape = im.shape[:2]
points[:, 0::2] = np.clip(points[:, 0::2], 0, img_shape[1])
points[:, 1::2] = np.clip(points[:, 1::2], 0, img_shape[0])
return points
ppocr/losses/__init__.py
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......@@ -35,6 +35,7 @@ from .cls_loss import ClsLoss
# e2e loss
from .e2e_pg_loss import PGLoss
from .kie_sdmgr_loss import SDMGRLoss
# basic loss function
from .basic_loss import DistanceLoss
......@@ -50,7 +51,7 @@ def build_loss(config):
support_dict = [
'DBLoss', 'PSELoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss',
'AttentionLoss', 'SRNLoss', 'PGLoss', 'CombinedLoss', 'NRTRLoss',
'TableAttentionLoss', 'SARLoss', 'AsterLoss'
'TableAttentionLoss', 'SARLoss', 'AsterLoss', 'SDMGRLoss'
]
config = copy.deepcopy(config)
module_name = config.pop('name')
......
ppocr/losses/kie_sdmgr_loss.py 0 → 100644
浏览文件 @ 86b90aa9
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from paddle import nn
import paddle
class SDMGRLoss(nn.Layer):
def __init__(self, node_weight=1.0, edge_weight=1.0, ignore=0):
super().__init__()
self.loss_node = nn.CrossEntropyLoss(ignore_index=ignore)
self.loss_edge = nn.CrossEntropyLoss(ignore_index=-1)
self.node_weight = node_weight
self.edge_weight = edge_weight
self.ignore = ignore
def pre_process(self, gts, tag):
gts, tag = gts.numpy(), tag.numpy().tolist()
temp_gts = []
batch = len(tag)
for i in range(batch):
num, recoder_len = tag[i][0], tag[i][1]
temp_gts.append(
paddle.to_tensor(
gts[i, :num, :num + 1], dtype='int64'))
return temp_gts
def accuracy(self, pred, target, topk=1, thresh=None):
"""Calculate accuracy according to the prediction and target.
Args:
pred (torch.Tensor): The model prediction, shape (N, num_class)
target (torch.Tensor): The target of each prediction, shape (N, )
topk (int | tuple[int], optional): If the predictions in ``topk``
matches the target, the predictions will be regarded as
correct ones. Defaults to 1.
thresh (float, optional): If not None, predictions with scores under
this threshold are considered incorrect. Default to None.
Returns:
float | tuple[float]: If the input ``topk`` is a single integer,
the function will return a single float as accuracy. If
``topk`` is a tuple containing multiple integers, the
function will return a tuple containing accuracies of
each ``topk`` number.
"""
assert isinstance(topk, (int, tuple))
if isinstance(topk, int):
topk = (topk, )
return_single = True
else:
return_single = False
maxk = max(topk)
if pred.shape[0] == 0:
accu = [pred.new_tensor(0.) for i in range(len(topk))]
return accu[0] if return_single else accu
pred_value, pred_label = paddle.topk(pred, maxk, axis=1)
pred_label = pred_label.transpose(
[1, 0]) # transpose to shape (maxk, N)
correct = paddle.equal(pred_label,
(target.reshape([1, -1]).expand_as(pred_label)))
res = []
for k in topk:
correct_k = paddle.sum(correct[:k].reshape([-1]).astype('float32'),
axis=0,
keepdim=True)
res.append(
paddle.multiply(correct_k,
paddle.to_tensor(100.0 / pred.shape[0])))
return res[0] if return_single else res
def forward(self, pred, batch):
node_preds, edge_preds = pred
gts, tag = batch[4], batch[5]
gts = self.pre_process(gts, tag)
node_gts, edge_gts = [], []
for gt in gts:
node_gts.append(gt[:, 0])
edge_gts.append(gt[:, 1:].reshape([-1]))
node_gts = paddle.concat(node_gts)
edge_gts = paddle.concat(edge_gts)
node_valids = paddle.nonzero(node_gts != self.ignore).reshape([-1])
edge_valids = paddle.nonzero(edge_gts != -1).reshape([-1])
loss_node = self.loss_node(node_preds, node_gts)
loss_edge = self.loss_edge(edge_preds, edge_gts)
loss = self.node_weight * loss_node + self.edge_weight * loss_edge
return dict(
loss=loss,
loss_node=loss_node,
loss_edge=loss_edge,
acc_node=self.accuracy(
paddle.gather(node_preds, node_valids),
paddle.gather(node_gts, node_valids)),
acc_edge=self.accuracy(
paddle.gather(edge_preds, edge_valids),
paddle.gather(edge_gts, edge_valids)))
ppocr/metrics/__init__.py
浏览文件 @ 86b90aa9
......@@ -27,10 +27,13 @@ from .cls_metric import ClsMetric
from .e2e_metric import E2EMetric
from .distillation_metric import DistillationMetric
from .table_metric import TableMetric
from .kie_metric import KIEMetric
def build_metric(config):
support_dict = [
"DetMetric", "RecMetric", "ClsMetric", "E2EMetric", "DistillationMetric", "TableMetric"
"DetMetric", "RecMetric", "ClsMetric", "E2EMetric",
"DistillationMetric", "TableMetric", 'KIEMetric'
]
config = copy.deepcopy(config)
......
ppocr/metrics/kie_metric.py 0 → 100644
浏览文件 @ 86b90aa9
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import paddle
__all__ = ['KIEMetric']
class KIEMetric(object):
def __init__(self, main_indicator='hmean', **kwargs):
self.main_indicator = main_indicator
self.reset()
self.node = []
self.gt = []
def __call__(self, preds, batch, **kwargs):
nodes, _ = preds
gts, tag = batch[4].squeeze(0), batch[5].tolist()[0]
gts = gts[:tag[0], :1].reshape([-1])
self.node.append(nodes.numpy())
self.gt.append(gts)
# result = self.compute_f1_score(nodes, gts)
# self.results.append(result)
def compute_f1_score(self, preds, gts):
ignores = [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 25]
C = preds.shape[1]
classes = np.array(sorted(set(range(C)) - set(ignores)))
hist = np.bincount(
(gts * C).astype('int64') + preds.argmax(1), minlength=C
**2).reshape([C, C]).astype('float32')
diag = np.diag(hist)
recalls = diag / hist.sum(1).clip(min=1)
precisions = diag / hist.sum(0).clip(min=1)
f1 = 2 * recalls * precisions / (recalls + precisions).clip(min=1e-8)
return f1[classes]
def combine_results(self, results):
node = np.concatenate(self.node, 0)
gts = np.concatenate(self.gt, 0)
results = self.compute_f1_score(node, gts)
data = {'hmean': results.mean()}
return data
def get_metric(self):
metircs = self.combine_results(self.results)
self.reset()
return metircs
def reset(self):
self.results = [] # clear results
self.node = []
self.gt = []
ppocr/metrics/rec_metric.py
浏览文件 @ 86b90aa9
......@@ -48,7 +48,7 @@ class RecMetric(object):
self.norm_edit_dis += norm_edit_dis
return {
'acc': correct_num / all_num,
'norm_edit_dis': 1 - norm_edit_dis / all_num
'norm_edit_dis': 1 - norm_edit_dis / (all_num + 1e-3)
}
def get_metric(self):
......@@ -58,8 +58,8 @@ class RecMetric(object):
'norm_edit_dis': 0,
}
"""
acc = 1.0 * self.correct_num / self.all_num
norm_edit_dis = 1 - self.norm_edit_dis / self.all_num
acc = 1.0 * self.correct_num / (self.all_num + 1e-3)
norm_edit_dis = 1 - self.norm_edit_dis / (self.all_num + 1e-3)
self.reset()
return {'acc': acc, 'norm_edit_dis': norm_edit_dis}
......
ppocr/modeling/backbones/__init__.py
浏览文件 @ 86b90aa9
......@@ -35,7 +35,14 @@ def build_backbone(config, model_type):
]
elif model_type == "e2e":
from .e2e_resnet_vd_pg import ResNet
support_dict = ["ResNet"]
support_dict = ['ResNet']
elif model_type == 'kie':
from .kie_unet_sdmgr import Kie_backbone
support_dict = ['Kie_backbone']
elif model_type == "table":
from .table_resnet_vd import ResNet
from .table_mobilenet_v3 import MobileNetV3
support_dict = ["ResNet", "MobileNetV3"]
else:
raise NotImplementedError
......
ppocr/modeling/backbones/kie_unet_sdmgr.py 0 → 100644
浏览文件 @ 86b90aa9
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
from paddle import nn
import numpy as np
import cv2
__all__ = ["Kie_backbone"]
class Encoder(nn.Layer):
def __init__(self, num_channels, num_filters):
super(Encoder, self).__init__()
self.conv1 = nn.Conv2D(
num_channels,
num_filters,
kernel_size=3,
stride=1,
padding=1,
bias_attr=False)
self.bn1 = nn.BatchNorm(num_filters, act='relu')
self.conv2 = nn.Conv2D(
num_filters,
num_filters,
kernel_size=3,
stride=1,
padding=1,
bias_attr=False)
self.bn2 = nn.BatchNorm(num_filters, act='relu')
self.pool = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
def forward(self, inputs):
x = self.conv1(inputs)
x = self.bn1(x)
x = self.conv2(x)
x = self.bn2(x)
x_pooled = self.pool(x)
return x, x_pooled
class Decoder(nn.Layer):
def __init__(self, num_channels, num_filters):
super(Decoder, self).__init__()
self.conv1 = nn.Conv2D(
num_channels,
num_filters,
kernel_size=3,
stride=1,
padding=1,
bias_attr=False)
self.bn1 = nn.BatchNorm(num_filters, act='relu')
self.conv2 = nn.Conv2D(
num_filters,
num_filters,
kernel_size=3,
stride=1,
padding=1,
bias_attr=False)
self.bn2 = nn.BatchNorm(num_filters, act='relu')
self.conv0 = nn.Conv2D(
num_channels,
num_filters,
kernel_size=1,
stride=1,
padding=0,
bias_attr=False)
self.bn0 = nn.BatchNorm(num_filters, act='relu')
def forward(self, inputs_prev, inputs):
x = self.conv0(inputs)
x = self.bn0(x)
x = paddle.nn.functional.interpolate(
x, scale_factor=2, mode='bilinear', align_corners=False)
x = paddle.concat([inputs_prev, x], axis=1)
x = self.conv1(x)
x = self.bn1(x)
x = self.conv2(x)
x = self.bn2(x)
return x
class UNet(nn.Layer):
def __init__(self):
super(UNet, self).__init__()
self.down1 = Encoder(num_channels=3, num_filters=16)
self.down2 = Encoder(num_channels=16, num_filters=32)
self.down3 = Encoder(num_channels=32, num_filters=64)
self.down4 = Encoder(num_channels=64, num_filters=128)
self.down5 = Encoder(num_channels=128, num_filters=256)
self.up1 = Decoder(32, 16)
self.up2 = Decoder(64, 32)
self.up3 = Decoder(128, 64)
self.up4 = Decoder(256, 128)
self.out_channels = 16
def forward(self, inputs):
x1, _ = self.down1(inputs)
_, x2 = self.down2(x1)
_, x3 = self.down3(x2)
_, x4 = self.down4(x3)
_, x5 = self.down5(x4)
x = self.up4(x4, x5)
x = self.up3(x3, x)
x = self.up2(x2, x)
x = self.up1(x1, x)
return x
class Kie_backbone(nn.Layer):
def __init__(self, in_channels, **kwargs):
super(Kie_backbone, self).__init__()
self.out_channels = 16
self.img_feat = UNet()
self.maxpool = nn.MaxPool2D(kernel_size=7)
def bbox2roi(self, bbox_list):
rois_list = []
rois_num = []
for img_id, bboxes in enumerate(bbox_list):
rois_num.append(bboxes.shape[0])
rois_list.append(bboxes)
rois = paddle.concat(rois_list, 0)
rois_num = paddle.to_tensor(rois_num, dtype='int32')
return rois, rois_num
def pre_process(self, img, relations, texts, gt_bboxes, tag, img_size):
img, relations, texts, gt_bboxes, tag, img_size = img.numpy(
), relations.numpy(), texts.numpy(), gt_bboxes.numpy(), tag.numpy(
).tolist(), img_size.numpy()
temp_relations, temp_texts, temp_gt_bboxes = [], [], []
h, w = int(np.max(img_size[:, 0])), int(np.max(img_size[:, 1]))
img = paddle.to_tensor(img[:, :, :h, :w])
batch = len(tag)
for i in range(batch):
num, recoder_len = tag[i][0], tag[i][1]
temp_relations.append(
paddle.to_tensor(
relations[i, :num, :num, :], dtype='float32'))
temp_texts.append(
paddle.to_tensor(
texts[i, :num, :recoder_len], dtype='float32'))
temp_gt_bboxes.append(
paddle.to_tensor(
gt_bboxes[i, :num, ...], dtype='float32'))
return img, temp_relations, temp_texts, temp_gt_bboxes
def forward(self, inputs):
img = inputs[0]
relations, texts, gt_bboxes, tag, img_size = inputs[1], inputs[
2], inputs[3], inputs[5], inputs[-1]
img, relations, texts, gt_bboxes = self.pre_process(
img, relations, texts, gt_bboxes, tag, img_size)
x = self.img_feat(img)
boxes, rois_num = self.bbox2roi(gt_bboxes)
feats = paddle.fluid.layers.roi_align(
x,
boxes,
spatial_scale=1.0,
pooled_height=7,
pooled_width=7,
rois_num=rois_num)
feats = self.maxpool(feats).squeeze(-1).squeeze(-1)
return [relations, texts, feats]
ppocr/modeling/heads/__init__.py
浏览文件 @ 86b90aa9
......@@ -33,14 +33,19 @@ def build_head(config):
# cls head
from .cls_head import ClsHead
#kie head
from .kie_sdmgr_head import SDMGRHead
from .table_att_head import TableAttentionHead
support_dict = [
'DBHead', 'PSEHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead',
'AttentionHead', 'SRNHead', 'PGHead', 'Transformer',
'TableAttentionHead', 'SARHead', 'AsterHead'
'TableAttentionHead', 'SARHead', 'AsterHead', 'SDMGRHead'
]
#table head
from .table_att_head import TableAttentionHead
module_name = config.pop('name')
assert module_name in support_dict, Exception('head only support {}'.format(
......
ppocr/modeling/heads/kie_sdmgr_head.py 0 → 100644
浏览文件 @ 86b90aa9
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
from paddle import nn
import paddle.nn.functional as F
from paddle import ParamAttr
class SDMGRHead(nn.Layer):
def __init__(self,
in_channels,
num_chars=92,
visual_dim=16,
fusion_dim=1024,
node_input=32,
node_embed=256,
edge_input=5,
edge_embed=256,
num_gnn=2,
num_classes=26,
bidirectional=False):
super().__init__()
self.fusion = Block([visual_dim, node_embed], node_embed, fusion_dim)
self.node_embed = nn.Embedding(num_chars, node_input, 0)
hidden = node_embed // 2 if bidirectional else node_embed
self.rnn = nn.LSTM(
input_size=node_input, hidden_size=hidden, num_layers=1)
self.edge_embed = nn.Linear(edge_input, edge_embed)
self.gnn_layers = nn.LayerList(
[GNNLayer(node_embed, edge_embed) for _ in range(num_gnn)])
self.node_cls = nn.Linear(node_embed, num_classes)
self.edge_cls = nn.Linear(edge_embed, 2)
def forward(self, input, targets):
relations, texts, x = input
node_nums, char_nums = [], []
for text in texts:
node_nums.append(text.shape[0])
char_nums.append(paddle.sum((text > -1).astype(int), axis=-1))
max_num = max([char_num.max() for char_num in char_nums])
all_nodes = paddle.concat([
paddle.concat(
[text, paddle.zeros(
(text.shape[0], max_num - text.shape[1]))], -1)
for text in texts
])
temp = paddle.clip(all_nodes, min=0).astype(int)
embed_nodes = self.node_embed(temp)
rnn_nodes, _ = self.rnn(embed_nodes)
b, h, w = rnn_nodes.shape
nodes = paddle.zeros([b, w])
all_nums = paddle.concat(char_nums)
valid = paddle.nonzero((all_nums > 0).astype(int))
temp_all_nums = (
paddle.gather(all_nums, valid) - 1).unsqueeze(-1).unsqueeze(-1)
temp_all_nums = paddle.expand(temp_all_nums, [
temp_all_nums.shape[0], temp_all_nums.shape[1], rnn_nodes.shape[-1]
])
temp_all_nodes = paddle.gather(rnn_nodes, valid)
N, C, A = temp_all_nodes.shape
one_hot = F.one_hot(
temp_all_nums[:, 0, :], num_classes=C).transpose([0, 2, 1])
one_hot = paddle.multiply(
temp_all_nodes, one_hot.astype("float32")).sum(axis=1, keepdim=True)
t = one_hot.expand([N, 1, A]).squeeze(1)
nodes = paddle.scatter(nodes, valid.squeeze(1), t)
if x is not None:
nodes = self.fusion([x, nodes])
all_edges = paddle.concat(
[rel.reshape([-1, rel.shape[-1]]) for rel in relations])
embed_edges = self.edge_embed(all_edges.astype('float32'))
embed_edges = F.normalize(embed_edges)
for gnn_layer in self.gnn_layers:
nodes, cat_nodes = gnn_layer(nodes, embed_edges, node_nums)
node_cls, edge_cls = self.node_cls(nodes), self.edge_cls(cat_nodes)
return node_cls, edge_cls
class GNNLayer(nn.Layer):
def __init__(self, node_dim=256, edge_dim=256):
super().__init__()
self.in_fc = nn.Linear(node_dim * 2 + edge_dim, node_dim)
self.coef_fc = nn.Linear(node_dim, 1)
self.out_fc = nn.Linear(node_dim, node_dim)
self.relu = nn.ReLU()
def forward(self, nodes, edges, nums):
start, cat_nodes = 0, []
for num in nums:
sample_nodes = nodes[start:start + num]
cat_nodes.append(
paddle.concat([
paddle.expand(sample_nodes.unsqueeze(1), [-1, num, -1]),
paddle.expand(sample_nodes.unsqueeze(0), [num, -1, -1])
], -1).reshape([num**2, -1]))
start += num
cat_nodes = paddle.concat([paddle.concat(cat_nodes), edges], -1)
cat_nodes = self.relu(self.in_fc(cat_nodes))
coefs = self.coef_fc(cat_nodes)
start, residuals = 0, []
for num in nums:
residual = F.softmax(
-paddle.eye(num).unsqueeze(-1) * 1e9 +
coefs[start:start + num**2].reshape([num, num, -1]), 1)
residuals.append((residual * cat_nodes[start:start + num**2]
.reshape([num, num, -1])).sum(1))
start += num**2
nodes += self.relu(self.out_fc(paddle.concat(residuals)))
return [nodes, cat_nodes]
class Block(nn.Layer):
def __init__(self,
input_dims,
output_dim,
mm_dim=1600,
chunks=20,
rank=15,
shared=False,
dropout_input=0.,
dropout_pre_lin=0.,
dropout_output=0.,
pos_norm='before_cat'):
super().__init__()
self.rank = rank
self.dropout_input = dropout_input
self.dropout_pre_lin = dropout_pre_lin
self.dropout_output = dropout_output
assert (pos_norm in ['before_cat', 'after_cat'])
self.pos_norm = pos_norm
# Modules
self.linear0 = nn.Linear(input_dims[0], mm_dim)
self.linear1 = (self.linear0
if shared else nn.Linear(input_dims[1], mm_dim))
self.merge_linears0 = nn.LayerList()
self.merge_linears1 = nn.LayerList()
self.chunks = self.chunk_sizes(mm_dim, chunks)
for size in self.chunks:
ml0 = nn.Linear(size, size * rank)
self.merge_linears0.append(ml0)
ml1 = ml0 if shared else nn.Linear(size, size * rank)
self.merge_linears1.append(ml1)
self.linear_out = nn.Linear(mm_dim, output_dim)
def forward(self, x):
x0 = self.linear0(x[0])
x1 = self.linear1(x[1])
bs = x1.shape[0]
if self.dropout_input > 0:
x0 = F.dropout(x0, p=self.dropout_input, training=self.training)
x1 = F.dropout(x1, p=self.dropout_input, training=self.training)
x0_chunks = paddle.split(x0, self.chunks, -1)
x1_chunks = paddle.split(x1, self.chunks, -1)
zs = []
for x0_c, x1_c, m0, m1 in zip(x0_chunks, x1_chunks, self.merge_linears0,
self.merge_linears1):
m = m0(x0_c) * m1(x1_c) # bs x split_size*rank
m = m.reshape([bs, self.rank, -1])
z = paddle.sum(m, 1)
if self.pos_norm == 'before_cat':
z = paddle.sqrt(F.relu(z)) - paddle.sqrt(F.relu(-z))
z = F.normalize(z)
zs.append(z)
z = paddle.concat(zs, 1)
if self.pos_norm == 'after_cat':
z = paddle.sqrt(F.relu(z)) - paddle.sqrt(F.relu(-z))
z = F.normalize(z)
if self.dropout_pre_lin > 0:
z = F.dropout(z, p=self.dropout_pre_lin, training=self.training)
z = self.linear_out(z)
if self.dropout_output > 0:
z = F.dropout(z, p=self.dropout_output, training=self.training)
return z
def chunk_sizes(self, dim, chunks):
split_size = (dim + chunks - 1) // chunks
sizes_list = [split_size] * chunks
sizes_list[-1] = sizes_list[-1] - (sum(sizes_list) - dim)
return sizes_list
ppocr/modeling/heads/rec_aster_head.py
浏览文件 @ 86b90aa9
......@@ -47,7 +47,7 @@ class AsterHead(nn.Layer):
self.time_step = time_step
self.embeder = Embedding(self.time_step, in_channels)
self.beam_width = beam_width
self.eos = self.num_classes - 1
self.eos = self.num_classes - 3
def forward(self, x, targets=None, embed=None):
return_dict = {}
......
ppocr/postprocess/__init__.py
浏览文件 @ 86b90aa9
......@@ -45,6 +45,8 @@ def build_post_process(config, global_config=None):
config = copy.deepcopy(config)
module_name = config.pop('name')
if module_name == "None":
return
if global_config is not None:
config.update(global_config)
assert module_name in support_dict, Exception(
......
ppocr/postprocess/rec_postprocess.py
浏览文件 @ 86b90aa9
......@@ -287,9 +287,12 @@ class SEEDLabelDecode(BaseRecLabelDecode):
use_space_char)
def add_special_char(self, dict_character):
self.beg_str = "sos"
self.padding_str = "padding"
self.end_str = "eos"
dict_character = dict_character + [self.end_str]
self.unknown = "unknown"
dict_character = dict_character + [
self.end_str, self.padding_str, self.unknown
]
return dict_character
def get_ignored_tokens(self):
......
ppstructure/README.md
浏览文件 @ 86b90aa9
......@@ -153,13 +153,12 @@ wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_in
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
cd ..
python3 predict_system.py --det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer --rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/ppocr_keys_v1.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=ch --output=../output/table --vis_font_path=../doc/fonts/simfang.ttf
python3 predict_system.py --det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer --rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/ppocr_keys_v1.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --output=../output/table --vis_font_path=../doc/fonts/simfang.ttf
```
After running, each image will have a directory with the same name under the directory specified in the output field. Each table in the picture will be stored as an excel and figure area will be cropped and saved, the excel and image file name will be the coordinates of the table in the image.
**Model List**
|model name|description|config|model size|download|
| --- | --- | --- | --- | --- |
|en_ppocr_mobile_v2.0_table_structure|Table structure prediction for English table scenarios|[table_mv3.yml](../configs/table/table_mv3.yml)|18.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) |
......@@ -184,4 +183,5 @@ OCR and table recognition model
|en_ppocr_mobile_v2.0_table_rec|Text recognition of English table scene trained on PubLayNet dataset|6.9M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar) [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_rec_train.tar) |
|en_ppocr_mobile_v2.0_table_structure|Table structure prediction of English table scene trained on PubLayNet dataset|18.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar) |
If you need to use other models, you can download the model in [model_list](../doc/doc_en/models_list_en.md) or use your own trained model to configure it to the three fields of `det_model_dir`, `rec_model_dir`, `table_model_dir` .
ppstructure/README_ch.md
浏览文件 @ 86b90aa9
[English](README.md) | 简体中文
# PP-Structure
## 简介
PP-Structure是一个可用于复杂文档结构分析和处理的OCR工具包，旨在帮助开发者更好的完成文档理解相关任务。
## 近期更新
* 2021.12.07 新增VQA任务-SER和RE。
## 特性
PP-Structure是一个可用于复杂文档结构分析和处理的OCR工具包，主要特性如下：
- 支持对图片形式的文档进行版面分析，可以划分**文字、标题、表格、图片以及列表**5类区域（与Layout-Parser联合使用）
......@@ -8,181 +14,88 @@ PP-Structure是一个可用于复杂文档结构分析和处理的OCR工具包
- 支持表格区域进行结构化分析，最终结果输出Excel文件
- 支持python whl包和命令行两种方式，简单易用
- 支持版面分析和表格结构化两类任务自定义训练
- 支持文档视觉问答(Document Visual Question Answering，DOC-VQA)任务-语义实体识别(Semantic Entity Recognition，SER)和关系抽取(Relation Extraction，RE)
## 1. 效果展示
<img src="../doc/table/ppstructure.GIF" width="100%"/>
## 2. 安装
### 2.1 安装依赖
- **（1) 安装PaddlePaddle**
```bash
pip3 install --upgrade pip
# GPU安装
python3 -m pip install paddlepaddle-gpu==2.1.1 -i https://mirror.baidu.com/pypi/simple
# CPU安装
python3 -m pip install paddlepaddle==2.1.1 -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) 安装 Layout-Parser**
```bash
pip3 install -U https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
```
### 2.2 安装PaddleOCR（包含PP-OCR和PP-Structure）
- **（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
```
- **（2) 完整克隆PaddleOCR源码（预测+训练）**
```bash
【推荐】git clone https://github.com/PaddlePaddle/PaddleOCR
#如果因为网络问题无法pull成功，也可选择使用码云上的托管：
git clone https://gitee.com/paddlepaddle/PaddleOCR
#注：码云托管代码可能无法实时同步本github项目更新，存在3~5天延时，请优先使用推荐方式。
```
## 3. PP-Structure 快速开始
### 3.1 命令行使用（默认参数，极简）
```bash
paddleocr --image_dir=../doc/table/1.png --type=structure
```
### 3.2 Python脚本使用（自定义参数，灵活）
## 1. 效果展示
```python
import os
import cv2
from paddleocr import PPStructure,draw_structure_result,save_structure_res
### 1.1 版面分析和表格识别
table_engine = PPStructure(show_log=True)
<img src="../doc/table/ppstructure.GIF" width="100%"/>
save_folder = './output/table'
img_path = '../doc/table/1.png'
img = cv2.imread(img_path)
result = table_engine(img)
save_structure_res(result, save_folder,os.path.basename(img_path).split('.')[0])
### 1.2 VQA
for line in result:
line.pop('img')
print(line)
* SER
from PIL import Image
![](./vqa/images/result_ser/zh_val_0_ser.jpg) | ![](./vqa/images/result_ser/zh_val_42_ser.jpg)
---|---
font_path = '../doc/fonts/simfang.ttf' # PaddleOCR下提供字体包
image = Image.open(img_path).convert('RGB')
im_show = draw_structure_result(image, result,font_path=font_path)
im_show = Image.fromarray(im_show)
im_show.save('result.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
### 3.3 返回结果说明
PP-Structure的返回结果为一个dict组成的list，示例如下
* 深紫色：HEADER
* 浅紫色：QUESTION
* 军绿色：ANSWER
```shell
[
{ 'type': 'Text',
'bbox': [34, 432, 345, 462],
'res': ([[36.0, 437.0, 341.0, 437.0, 341.0, 446.0, 36.0, 447.0], [41.0, 454.0, 125.0, 453.0, 125.0, 459.0, 41.0, 460.0]],
[('Tigure-6. The performance of CNN and IPT models using difforen', 0.90060663), ('Tent ', 0.465441)])
}
]
```
dict 里各个字段说明如下
在OCR检测框的左上方也标出了对应的类别和OCR识别结果。
| 字段 | 说明 |
| --------------- | -------------|
|type|图片区域的类型|
|bbox|图片区域的在原图的坐标，分别[左上角x，左上角y，右下角x，右下角y]|
|res|图片区域的OCR或表格识别结果。<br> 表格: 表格的HTML字符串; <br> OCR: 一个包含各个单行文字的检测坐标和识别结果的元组|
* RE
![](./vqa/images/result_re/zh_val_21_re.jpg) | ![](./vqa/images/result_re/zh_val_40_re.jpg)
---|---
### 3.4 参数说明
| 字段 | 说明 | 默认值 |
| --------------- | ---------------------------------------- | ------------------------------------------- |
| output | excel和识别结果保存的地址 | ./output/table |
| table_max_len | 表格结构模型预测时，图像的长边resize尺度 | 488 |
| table_model_dir | 表格结构模型 inference 模型地址 | None |
| table_char_type | 表格结构模型所用字典地址 | ../ppocr/utils/dict/table_structure_dict.tx |
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。
大部分参数和paddleocr whl包保持一致，见 [whl包文档](../doc/doc_ch/whl.md)
## 2. 快速体验
运行完成后，每张图片会在`output`字段指定的目录下有一个同名目录，图片里的每个表格会存储为一个excel，图片区域会被裁剪之后保存下来，excel文件和图片名名为表格在图片里的坐标。
代码体验：从 [快速安装](./docs/quickstart.md) 开始
## 3. PP-Structure Pipeline介绍
## 4. PP-Structure Pipeline介绍
### 3.1 版面分析+表格识别
![pipeline](../doc/table/pipeline.jpg)
在PP-Structure中，图片会先经由Layout-Parser进行版面分析，在版面分析中，会对图片里的区域进行分类，包括**文字、标题、图片、列表和表格**5类。对于前4类区域，直接使用PP-OCR完成对应区域文字检测与识别。对于表格类区域，经过表格结构化处理后，表格图片转换为相同表格样式的Excel文件。
### 4.1 版面分析
#### 3.1.1 版面分析
版面分析对文档数据进行区域分类，其中包括版面分析工具的Python脚本使用、提取指定类别检测框、性能指标以及自定义训练版面分析模型，详细内容可以参考[文档](layout/README_ch.md)。
### 4.2 表格识别
#### 3.1.2 表格识别
表格识别将表格图片转换为excel文档，其中包含对于表格文本的检测和识别以及对于表格结构和单元格坐标的预测，详细说明参考[文档](table/README_ch.md)
## 5. 预测引擎推理（与whl包效果相同）
使用如下命令即可完成预测引擎的推理
### 3.2 VQA
```python
cd ppstructure
coming soon
# 下载模型
mkdir inference && cd inference
# 下载超轻量级中文OCR模型的检测模型并解压
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar
# 下载超轻量级中文OCR模型的识别模型并解压
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
# 下载超轻量级英文表格英寸模型并解压
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
cd ..
## 4. 模型库
python3 predict_system.py --det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer --rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/ppocr_keys_v1.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=ch --output=../output/table --vis_font_path=../doc/fonts/simfang.ttf
```
运行完成后，每张图片会在`output`字段指定的目录下有一个同名目录，图片里的每个表格会存储为一个excel，图片区域会被裁剪之后保存下来，excel文件和图片名名为表格在图片里的坐标。
PP-Structure系列模型列表（更新中）
**Model List**
LayoutParser 模型
* LayoutParser 模型
|模型名称|模型简介|下载地址|
| --- | --- | --- |
| ppyolov2_r50vd_dcn_365e_publaynet | PubLayNet 数据集训练的版面分析模型，可以划分**文字、标题、表格、图片以及列表**5类区域 | [PubLayNet](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet.tar) |
| ppyolov2_r50vd_dcn_365e_tableBank_word | TableBank Word 数据集训练的版面分析模型，只能检测表格 | [TableBank Word](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_word.tar) |
| ppyolov2_r50vd_dcn_365e_tableBank_latex | TableBank Latex 数据集训练的版面分析模型，只能检测表格 | [TableBank Latex](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_latex.tar) |
OCR和表格识别模型
|模型名称|模型简介|推理模型大小|下载地址|
* OCR和表格识别模型
|模型名称|模型简介|模型大小|下载地址|
| --- | --- | --- | --- |
|ch_ppocr_mobile_slim_v2.0_det|slim裁剪版超轻量模型，支持中英文、多语种文本检测|2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar) |
|ch_ppocr_mobile_slim_v2.0_rec|slim裁剪量化版超轻量模型，支持中英文、数字识别|6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_train.tar) |
|en_ppocr_mobile_v2.0_table_det|PubLayNet数据集训练的英文表格场景的文字检测|4.7M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_det_train.tar) |
|en_ppocr_mobile_v2.0_table_rec|PubLayNet数据集训练的英文表格场景的文字识别|6.9M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_rec_train.tar) |
|en_ppocr_mobile_v2.0_table_structure|PubLayNet数据集训练的英文表格场景的表格结构预测|18.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar) |
如需要使用其他模型，可以在 [model_list](../doc/doc_ch/models_list.md) 下载模型或者使用自己训练好的模型配置到`det_model_dir`,`rec_model_dir`,`table_model_dir`三个字段即可。
* VQA模型
|模型名称|模型简介|模型大小|下载地址|
| --- | --- | --- | --- |
|PP-Layout_v1.0_ser_pretrained|基于LayoutXLM在xfun中文数据集上训练的SER模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_ser_pretrained.tar) |
|PP-Layout_v1.0_re_pretrained|基于LayoutXLM在xfun中文数据集上训练的RE模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_re_pretrained.tar) |
更多模型下载，可以参考 [模型库](./docs/model_list.md)
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# 快速安装
## 1. PaddlePaddle 和 PaddleOCR
可参考[PaddleOCR安装文档](../../doc/doc_ch/installation.md)
## 2. 安装其他依赖
### 2.1 版面分析所需 Layout-Parser
Layout-Parser 可通过如下命令安装
```bash
pip3 install -U https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
```
### 2.2 VQA所需依赖
* paddleocr
```bash
pip3 install paddleocr
```
* PaddleNLP
```bash
git clone https://github.com/PaddlePaddle/PaddleNLP -b develop
cd PaddleNLP
pip3 install -e .
```
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# 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](#1-----)
* [2. 执行训练](#2-----)
* [3. 执行评估](#3-----)
<a name="1-----"></a>
## 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/dygraph_v2.1/kie/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：
<div align="center">
<img src="./imgs/0.png" width="800">
</div>
<a name="2-----"></a>
## 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/
```
<a name="3-----"></a>
## 3. 执行评估
```
python3.7 tools/eval.py -c configs/kie/kie_unet_sdmgr.yml -o Global.checkpoints=./output/kie/best_accuracy
```
**参考文献：**
<!-- [ALGORITHM] -->
```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}
}
```
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# Model List
## 1. LayoutParser 模型
|模型名称|模型简介|下载地址|
| --- | --- | --- |
| ppyolov2_r50vd_dcn_365e_publaynet | PubLayNet 数据集训练的版面分析模型，可以划分**文字、标题、表格、图片以及列表**5类区域 | [PubLayNet](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet.tar) |
| ppyolov2_r50vd_dcn_365e_tableBank_word | TableBank Word 数据集训练的版面分析模型，只能检测表格 | [TableBank Word](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_word.tar) |
| ppyolov2_r50vd_dcn_365e_tableBank_latex | TableBank Latex 数据集训练的版面分析模型，只能检测表格 | [TableBank Latex](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_latex.tar) |
## 2. OCR和表格识别模型
|模型名称|模型简介|推理模型大小|下载地址|
| --- | --- | --- | --- |
|ch_ppocr_mobile_slim_v2.0_det|slim裁剪版超轻量模型，支持中英文、多语种文本检测|2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar) |
|ch_ppocr_mobile_slim_v2.0_rec|slim裁剪量化版超轻量模型，支持中英文、数字识别|6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_train.tar) |
|en_ppocr_mobile_v2.0_table_det|PubLayNet数据集训练的英文表格场景的文字检测|4.7M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_det_train.tar) |
|en_ppocr_mobile_v2.0_table_rec|PubLayNet数据集训练的英文表格场景的文字识别|6.9M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_rec_train.tar) |
|en_ppocr_mobile_v2.0_table_structure|PubLayNet数据集训练的英文表格场景的表格结构预测|18.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar) |
如需要使用其他OCR模型，可以在 [model_list](../../doc/doc_ch/models_list.md) 下载模型或者使用自己训练好的模型配置到`det_model_dir`,`rec_model_dir`两个字段即可。
## 3. VQA模型
|模型名称|模型简介|推理模型大小|下载地址|
| --- | --- | --- | --- |
|PP-Layout_v1.0_ser_pretrained|基于LayoutXLM在xfun中文数据集上训练的SER模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_ser_pretrained.tar) |
|PP-Layout_v1.0_re_pretrained|基于LayoutXLM在xfun中文数据集上训练的RE模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_re_pretrained.tar) |
## 3. KIE模型
|模型名称|模型简介|模型大小|下载地址|
| --- | --- | --- | --- |
|SDMGR|关键信息提取模型|-|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)|
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# PP-Structure 快速开始
* [1. 安装PaddleOCR whl包](#1)
* [2. 便捷使用](#2)
+ [2.1 命令行使用](#21)
+ [2.2 Python脚本使用](#22)
+ [2.3 返回结果说明](#23)
+ [2.4 参数说明](#24)
* [3. Python脚本使用](#3)
<a name="1"></a>
## 1. 安装依赖包
```bash
pip install "paddleocr>=2.3.0.2" # 推荐使用2.3.0.2+版本
pip3 install -U https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
# 安装 PaddleNLP
git clone https://github.com/PaddlePaddle/PaddleNLP -b develop
cd PaddleNLP
pip3 install -e .
```
<a name="2"></a>
## 2. 便捷使用
<a name="21"></a>
### 2.1 命令行使用
* 版面分析+表格识别
```bash
paddleocr --image_dir=../doc/table/1.png --type=structure
```
* VQA
coming soon
<a name="22"></a>
### 2.2 Python脚本使用
* 版面分析+表格识别
```python
import os
import cv2
from paddleocr import PPStructure,draw_structure_result,save_structure_res
table_engine = PPStructure(show_log=True)
save_folder = './output/table'
img_path = '../doc/table/1.png'
img = cv2.imread(img_path)
result = table_engine(img)
save_structure_res(result, save_folder,os.path.basename(img_path).split('.')[0])
for line in result:
line.pop('img')
print(line)
from PIL import Image
font_path = '../doc/fonts/simfang.ttf' # PaddleOCR下提供字体包
image = Image.open(img_path).convert('RGB')
im_show = draw_structure_result(image, result,font_path=font_path)
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
```
* VQA
comming soon
<a name="23"></a>
### 2.3 返回结果说明
PP-Structure的返回结果为一个dict组成的list，示例如下
* 版面分析+表格识别
```shell
[
{ 'type': 'Text',
'bbox': [34, 432, 345, 462],
'res': ([[36.0, 437.0, 341.0, 437.0, 341.0, 446.0, 36.0, 447.0], [41.0, 454.0, 125.0, 453.0, 125.0, 459.0, 41.0, 460.0]],
[('Tigure-6. The performance of CNN and IPT models using difforen', 0.90060663), ('Tent ', 0.465441)])
}
]
```
dict 里各个字段说明如下
| 字段 | 说明 |
| --------------- | -------------|
|type|图片区域的类型|
|bbox|图片区域的在原图的坐标，分别[左上角x，左上角y，右下角x，右下角y]|
|res|图片区域的OCR或表格识别结果。<br> 表格: 表格的HTML字符串; <br> OCR: 一个包含各个单行文字的检测坐标和识别结果的元组|
* VQA
comming soon
<a name="24"></a>
### 2.4 参数说明
| 字段 | 说明 | 默认值 |
| --------------- | ---------------------------------------- | ------------------------------------------- |
| output | excel和识别结果保存的地址 | ./output/table |
| table_max_len | 表格结构模型预测时，图像的长边resize尺度 | 488 |
| table_model_dir | 表格结构模型 inference 模型地址 | None |
| table_char_type | 表格结构模型所用字典地址 | ../ppocr/utils/dict/table_structure_dict.txt |
| model_name_or_path | VQA SER模型地址 | None |
| max_seq_length | VQA SER模型最大支持token长度 | 512 |
| label_map_path | VQA SER 标签文件地址 | ./vqa/labels/labels_ser.txt |
| mode | pipeline预测模式，structure: 版面分析+表格识别; vqa: ser文档信息抽取 | structure |
大部分参数和paddleocr whl包保持一致，见 [whl包文档](../doc/doc_ch/whl.md)
运行完成后，每张图片会在`output`字段指定的目录下有一个同名目录，图片里的每个表格会存储为一个excel，图片区域会被裁剪之后保存下来，excel文件和图片名名为表格在图片里的坐标。
<a name="3"></a>
## 3. Python脚本使用
* 版面分析+表格识别
```bash
cd ppstructure
# 下载模型
mkdir inference && cd inference
# 下载超轻量级中文OCR模型的检测模型并解压
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar
# 下载超轻量级中文OCR模型的识别模型并解压
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
# 下载超轻量级英文表格英寸模型并解压
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
cd ..
python3 predict_system.py --det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer \
--rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer \
--table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer \
--image_dir=../doc/table/1.png \
--rec_char_dict_path=../ppocr/utils/ppocr_keys_v1.txt \
--table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt \
--output=../output/table \
--vis_font_path=../doc/fonts/simfang.ttf
```
运行完成后，每张图片会在`output`字段指定的目录下的`talbe`目录下有一个同名目录，图片里的每个表格会存储为一个excel，图片区域会被裁剪之后保存下来，excel文件和图片名名为表格在图片里的坐标。
* VQA
```bash
cd ppstructure
# 下载模型
mkdir inference && cd inference
# 下载SER xfun 模型并解压
wget https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_ser_pretrained.tar && tar xf PP-Layout_v1.0_ser_pretrained.tar
cd ..
python3 predict_system.py --model_name_or_path=vqa/PP-Layout_v1.0_ser_pretrained/ \
--mode=vqa \
--image_dir=vqa/images/input/zh_val_0.jpg \
--vis_font_path=../doc/fonts/simfang.ttf
```
运行完成后，每张图片会在`output`字段指定的目录下的`vqa`目录下存放可视化之后的图片，图片名和输入图片名一致。
ppstructure/predict_system.py
浏览文件 @ 86b90aa9
......@@ -30,6 +30,7 @@ from ppocr.utils.utility import get_image_file_list, check_and_read_gif
from ppocr.utils.logging import get_logger
from tools.infer.predict_system import TextSystem
from ppstructure.table.predict_table import TableSystem, to_excel
from ppstructure.vqa.infer_ser_e2e import SerPredictor, draw_ser_results
from ppstructure.utility import parse_args, draw_structure_result
logger = get_logger()
......@@ -37,53 +38,75 @@ logger = get_logger()
class OCRSystem(object):
def __init__(self, args):
import layoutparser as lp
# args.det_limit_type = 'resize_long'
args.drop_score = 0
if not args.show_log:
logger.setLevel(logging.INFO)
self.text_system = TextSystem(args)
self.table_system = TableSystem(args, self.text_system.text_detector, self.text_system.text_recognizer)
config_path = None
model_path = None
if os.path.isdir(args.layout_path_model):
model_path = args.layout_path_model
else:
config_path = args.layout_path_model
self.table_layout = lp.PaddleDetectionLayoutModel(config_path=config_path,
model_path=model_path,
threshold=0.5, enable_mkldnn=args.enable_mkldnn,
enforce_cpu=not args.use_gpu, thread_num=args.cpu_threads)
self.use_angle_cls = args.use_angle_cls
self.drop_score = args.drop_score
self.mode = args.mode
if self.mode == 'structure':
import layoutparser as lp
# args.det_limit_type = 'resize_long'
args.drop_score = 0
if not args.show_log:
logger.setLevel(logging.INFO)
self.text_system = TextSystem(args)
self.table_system = TableSystem(args,
self.text_system.text_detector,
self.text_system.text_recognizer)
config_path = None
model_path = None
if os.path.isdir(args.layout_path_model):
model_path = args.layout_path_model
else:
config_path = args.layout_path_model
self.table_layout = lp.PaddleDetectionLayoutModel(
config_path=config_path,
model_path=model_path,
threshold=0.5,
enable_mkldnn=args.enable_mkldnn,
enforce_cpu=not args.use_gpu,
thread_num=args.cpu_threads)
self.use_angle_cls = args.use_angle_cls
self.drop_score = args.drop_score
elif self.mode == 'vqa':
self.vqa_engine = SerPredictor(args)
def __call__(self, img):
ori_im = img.copy()
layout_res = self.table_layout.detect(img[..., ::-1])
res_list = []
for region in layout_res:
x1, y1, x2, y2 = region.coordinates
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
roi_img = ori_im[y1:y2, x1:x2, :]
if region.type == 'Table':
res = self.table_system(roi_img)
else:
filter_boxes, filter_rec_res = self.text_system(roi_img)
filter_boxes = [x + [x1, y1] for x in filter_boxes]
filter_boxes = [x.reshape(-1).tolist() for x in filter_boxes]
# remove style char
style_token = ['<strike>', '<strike>', '<sup>', '</sub>', '<b>', '</b>', '<sub>', '</sup>',
'<overline>', '</overline>', '<underline>', '</underline>', '<i>', '</i>']
filter_rec_res_tmp = []
for rec_res in filter_rec_res:
rec_str, rec_conf = rec_res
for token in style_token:
if token in rec_str:
rec_str = rec_str.replace(token, '')
filter_rec_res_tmp.append((rec_str, rec_conf))
res = (filter_boxes, filter_rec_res_tmp)
res_list.append({'type': region.type, 'bbox': [x1, y1, x2, y2], 'img': roi_img, 'res': res})
if self.mode == 'structure':
ori_im = img.copy()
layout_res = self.table_layout.detect(img[..., ::-1])
res_list = []
for region in layout_res:
x1, y1, x2, y2 = region.coordinates
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
roi_img = ori_im[y1:y2, x1:x2, :]
if region.type == 'Table':
res = self.table_system(roi_img)
else:
filter_boxes, filter_rec_res = self.text_system(roi_img)
filter_boxes = [x + [x1, y1] for x in filter_boxes]
filter_boxes = [
x.reshape(-1).tolist() for x in filter_boxes
]
# remove style char
style_token = [
'<strike>', '<strike>', '<sup>', '</sub>', '<b>',
'</b>', '<sub>', '</sup>', '<overline>', '</overline>',
'<underline>', '</underline>', '<i>', '</i>'
]
filter_rec_res_tmp = []
for rec_res in filter_rec_res:
rec_str, rec_conf = rec_res
for token in style_token:
if token in rec_str:
rec_str = rec_str.replace(token, '')
filter_rec_res_tmp.append((rec_str, rec_conf))
res = (filter_boxes, filter_rec_res_tmp)
res_list.append({
'type': region.type,
'bbox': [x1, y1, x2, y2],
'img': roi_img,
'res': res
})
elif self.mode == 'vqa':
res_list, _ = self.vqa_engine(img)
return res_list
......@@ -91,29 +114,35 @@ def save_structure_res(res, save_folder, img_name):
excel_save_folder = os.path.join(save_folder, img_name)
os.makedirs(excel_save_folder, exist_ok=True)
# save res
with open(os.path.join(excel_save_folder, 'res.txt'), 'w', encoding='utf8') as f:
with open(
os.path.join(excel_save_folder, 'res.txt'), 'w',
encoding='utf8') as f:
for region in res:
if region['type'] == 'Table':
excel_path = os.path.join(excel_save_folder, '{}.xlsx'.format(region['bbox']))
excel_path = os.path.join(excel_save_folder,
'{}.xlsx'.format(region['bbox']))
to_excel(region['res'], excel_path)
if region['type'] == 'Figure':
roi_img = region['img']
img_path = os.path.join(excel_save_folder, '{}.jpg'.format(region['bbox']))
img_path = os.path.join(excel_save_folder,
'{}.jpg'.format(region['bbox']))
cv2.imwrite(img_path, roi_img)
else:
for box, rec_res in zip(region['res'][0], region['res'][1]):
f.write('{}\t{}\n'.format(np.array(box).reshape(-1).tolist(), rec_res))
f.write('{}\t{}\n'.format(
np.array(box).reshape(-1).tolist(), rec_res))
def main(args):
image_file_list = get_image_file_list(args.image_dir)
image_file_list = image_file_list
image_file_list = image_file_list[args.process_id::args.total_process_num]
save_folder = args.output
os.makedirs(save_folder, exist_ok=True)
structure_sys = OCRSystem(args)
img_num = len(image_file_list)
save_folder = os.path.join(args.output, structure_sys.mode)
os.makedirs(save_folder, exist_ok=True)
for i, image_file in enumerate(image_file_list):
logger.info("[{}/{}] {}".format(i, img_num, image_file))
img, flag = check_and_read_gif(image_file)
......@@ -126,10 +155,16 @@ def main(args):
continue
starttime = time.time()
res = structure_sys(img)
save_structure_res(res, save_folder, img_name)
draw_img = draw_structure_result(img, res, args.vis_font_path)
cv2.imwrite(os.path.join(save_folder, img_name, 'show.jpg'), draw_img)
logger.info('result save to {}'.format(os.path.join(save_folder, img_name)))
if structure_sys.mode == 'structure':
save_structure_res(res, save_folder, img_name)
draw_img = draw_structure_result(img, res, args.vis_font_path)
img_save_path = os.path.join(save_folder, img_name, 'show.jpg')
elif structure_sys.mode == 'vqa':
draw_img = draw_ser_results(img, res, args.vis_font_path)
img_save_path = os.path.join(save_folder, img_name + '.jpg')
cv2.imwrite(img_save_path, draw_img)
logger.info('result save to {}'.format(img_save_path))
elapse = time.time() - starttime
logger.info("Predict time : {:.3f}s".format(elapse))
......
ppstructure/table/README.md
浏览文件 @ 86b90aa9
......@@ -20,9 +20,9 @@ We evaluated the algorithm on the PubTabNet<sup>[1]</sup> eval dataset, and the
|Method|[TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)|
| --- | --- |
| EDD<sup>[2]</sup> | 88.3 |
| Ours | 93.32 |
| --- | --- |
| EDD<sup>[2]</sup> | 88.3 |
| Ours | 93.32 |
## 3. How to use
......@@ -41,7 +41,7 @@ wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_tab
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
cd ..
# run
python3 table/predict_table.py --det_model_dir=inference/en_ppocr_mobile_v2.0_table_det_infer --rec_model_dir=inference/en_ppocr_mobile_v2.0_table_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --output ../output/table
python3 table/predict_table.py --det_model_dir=inference/en_ppocr_mobile_v2.0_table_det_infer --rec_model_dir=inference/en_ppocr_mobile_v2.0_table_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_dict_path=../ppocr/utils/dict/en_dict.txt --det_limit_side_len=736 --det_limit_type=min --output ../output/table
```
Note: The above model is trained on the PubLayNet dataset and only supports English scanning scenarios. If you need to identify other scenarios, you need to train the model yourself and replace the three fields `det_model_dir`, `rec_model_dir`, `table_model_dir`.
......@@ -82,8 +82,8 @@ python3 tools/train.py -c configs/table/table_mv3.yml -o Global.checkpoints=./yo
The table uses [TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src) as the evaluation metric of the model. Before the model evaluation, the three models in the pipeline need to be exported as inference models (we have provided them), and the gt for evaluation needs to be prepared. Examples of gt are as follows:
```json
{"PMC4289340_004_00.png": [
["<html>", "<body>", "<table>", "<thead>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</thead>", "<tbody>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</tbody>", "</table>", "</body>", "</html>"],
[[1, 4, 29, 13], [137, 4, 161, 13], [215, 4, 236, 13], [1, 17, 30, 27], [137, 17, 147, 27], [215, 17, 225, 27]],
["<html>", "<body>", "<table>", "<thead>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</thead>", "<tbody>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</tbody>", "</table>", "</body>", "</html>"],
[[1, 4, 29, 13], [137, 4, 161, 13], [215, 4, 236, 13], [1, 17, 30, 27], [137, 17, 147, 27], [215, 17, 225, 27]],
[["<b>", "F", "e", "a", "t", "u", "r", "e", "</b>"], ["<b>", "G", "b", "3", " ", "+", "</b>"], ["<b>", "G", "b", "3", " ", "-", "</b>"], ["<b>", "P", "a", "t", "i", "e", "n", "t", "s", "</b>"], ["6", "2"], ["4", "5"]]
]}
```
......@@ -95,7 +95,7 @@ In gt json, the key is the image name, the value is the corresponding gt, and gt
Use the following command to evaluate. After the evaluation is completed, the teds indicator will be output.
```python
cd PaddleOCR/ppstructure
python3 table/eval_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --gt_path=path/to/gt.json
python3 table/eval_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --det_limit_side_len=736 --det_limit_type=min --gt_path=path/to/gt.json
```
If the PubLatNet eval dataset is used, it will be output
......@@ -113,4 +113,4 @@ After running, the excel sheet of each picture will be saved in the directory sp
Reference
1. https://github.com/ibm-aur-nlp/PubTabNet
2. https://arxiv.org/pdf/1911.10683
\ No newline at end of file
2. https://arxiv.org/pdf/1911.10683
ppstructure/table/README_ch.md
浏览文件 @ 86b90aa9
......@@ -34,9 +34,9 @@
|算法|[TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)|
| --- | --- |
| EDD<sup>[2]</sup> | 88.3 |
| Ours | 93.32 |
| --- | --- |
| EDD<sup>[2]</sup> | 88.3 |
| Ours | 93.32 |
<a name="3"></a>
## 3. 使用
......@@ -56,7 +56,7 @@ wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_tab
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
cd ..
# 执行预测
python3 table/predict_table.py --det_model_dir=inference/en_ppocr_mobile_v2.0_table_det_infer --rec_model_dir=inference/en_ppocr_mobile_v2.0_table_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --output ../output/table
python3 table/predict_table.py --det_model_dir=inference/en_ppocr_mobile_v2.0_table_det_infer --rec_model_dir=inference/en_ppocr_mobile_v2.0_table_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_dict_path=../ppocr/utils/dict/en_dict.txt --det_limit_side_len=736 --det_limit_type=min --output ../output/table
```
运行完成后，每张图片的excel表格会保存到output字段指定的目录下
......@@ -94,8 +94,8 @@ python3 tools/train.py -c configs/table/table_mv3.yml -o Global.checkpoints=./yo
表格使用 [TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src) 作为模型的评估指标。在进行模型评估之前，需要将pipeline中的三个模型分别导出为inference模型(我们已经提供好)，还需要准备评估的gt， gt示例如下:
```json
{"PMC4289340_004_00.png": [
["<html>", "<body>", "<table>", "<thead>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</thead>", "<tbody>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</tbody>", "</table>", "</body>", "</html>"],
[[1, 4, 29, 13], [137, 4, 161, 13], [215, 4, 236, 13], [1, 17, 30, 27], [137, 17, 147, 27], [215, 17, 225, 27]],
["<html>", "<body>", "<table>", "<thead>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</thead>", "<tbody>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</tbody>", "</table>", "</body>", "</html>"],
[[1, 4, 29, 13], [137, 4, 161, 13], [215, 4, 236, 13], [1, 17, 30, 27], [137, 17, 147, 27], [215, 17, 225, 27]],
[["<b>", "F", "e", "a", "t", "u", "r", "e", "</b>"], ["<b>", "G", "b", "3", " ", "+", "</b>"], ["<b>", "G", "b", "3", " ", "-", "</b>"], ["<b>", "P", "a", "t", "i", "e", "n", "t", "s", "</b>"], ["6", "2"], ["4", "5"]]
]}
```
......@@ -107,7 +107,7 @@ json 中，key为图片名，value为对应的gt，gt是一个由三个item组
准备完成后使用如下命令进行评估，评估完成后会输出teds指标。
```python
cd PaddleOCR/ppstructure
python3 table/eval_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --gt_path=path/to/gt.json
python3 table/eval_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --det_limit_side_len=736 --det_limit_type=min --gt_path=path/to/gt.json
```
如使用PubLatNet评估数据集，将会输出
```bash
......@@ -123,4 +123,4 @@ python3 table/predict_table.py --det_model_dir=path/to/det_model_dir --rec_model
Reference
1. https://github.com/ibm-aur-nlp/PubTabNet
2. https://arxiv.org/pdf/1911.10683
\ No newline at end of file
2. https://arxiv.org/pdf/1911.10683
ppstructure/utility.py
浏览文件 @ 86b90aa9
......@@ -21,13 +21,31 @@ def init_args():
parser = infer_args()
# params for output
parser.add_argument("--output", type=str, default='./output/table')
parser.add_argument("--output", type=str, default='./output')
# params for table structure
parser.add_argument("--table_max_len", type=int, default=488)
parser.add_argument("--table_model_dir", type=str)
parser.add_argument("--table_char_type", type=str, default='en')
parser.add_argument("--table_char_dict_path", type=str, default="../ppocr/utils/dict/table_structure_dict.txt")
parser.add_argument("--layout_path_model", type=str, default="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config")
parser.add_argument(
"--table_char_dict_path",
type=str,
default="../ppocr/utils/dict/table_structure_dict.txt")
parser.add_argument(
"--layout_path_model",
type=str,
default="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config")
# params for ser
parser.add_argument("--model_name_or_path", type=str)
parser.add_argument("--max_seq_length", type=int, default=512)
parser.add_argument(
"--label_map_path", type=str, default='./vqa/labels/labels_ser.txt')
parser.add_argument(
"--mode",
type=str,
default='structure',
help='structure and vqa is supported')
return parser
......@@ -48,5 +66,6 @@ def draw_structure_result(image, result, font_path):
boxes.append(np.array(box).reshape(-1, 2))
txts.append(rec_res[0])
scores.append(rec_res[1])
im_show = draw_ocr_box_txt(image, boxes, txts, scores, font_path=font_path,drop_score=0)
return im_show
\ No newline at end of file
im_show = draw_ocr_box_txt(
image, boxes, txts, scores, font_path=font_path, drop_score=0)
return im_show
ppstructure/vqa/README.md
浏览文件 @ 86b90aa9
......@@ -18,12 +18,13 @@ PP-Structure 里的 DOC-VQA算法基于PaddleNLP自然语言处理算法库进
## 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
我们在 [XFUN](https://github.com/doc-analysis/XFUND) 的中文数据集上对算法进行了评估，性能如下
|任务| f1 | 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)|
| 模型 | 任务 | f1 | 模型下载地址 |
|:---:|:---:|:---:| :---:|
| LayoutXLM | RE | 0.7113 | [链接](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_re_pretrained.tar) |
| LayoutXLM | SER | 0.9056 | [链接](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_ser_pretrained.tar) |
| LayoutLM | SER | 0.78 | [链接](https://paddleocr.bj.bcebos.com/pplayout/LayoutLM_ser_pretrained.tar) |
......@@ -98,7 +99,7 @@ git clone https://gitee.com/paddlepaddle/PaddleOCR
# 需要使用PaddleNLP最新的代码版本进行安装
git clone https://github.com/PaddlePaddle/PaddleNLP -b develop
cd PaddleNLP
pip install -e .
pip3 install -e .
```
......@@ -135,13 +136,13 @@ wget https://paddleocr.bj.bcebos.com/dataset/XFUND.tar
```shell
python3.7 train_ser.py \
--model_name_or_path "layoutxlm-base-uncased" \
--ser_model_type "LayoutXLM" \
--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" \
--num_train_epochs 200 \
--eval_steps 10 \
--save_steps 500 \
--output_dir "./output/ser/" \
--learning_rate 5e-5 \
--warmup_steps 50 \
......@@ -151,13 +152,50 @@ python3.7 train_ser.py \
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。
* 恢复训练
```shell
python3.7 train_ser.py \
--model_name_or_path "model_path" \
--ser_model_type "LayoutXLM" \
--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" \
--num_train_epochs 200 \
--eval_steps 10 \
--output_dir "./output/ser/" \
--learning_rate 5e-5 \
--warmup_steps 50 \
--evaluate_during_training \
--num_workers 8 \
--seed 2048 \
--resume
```
* 评估
```shell
export CUDA_VISIBLE_DEVICES=0
python3 eval_ser.py \
--model_name_or_path "PP-Layout_v1.0_ser_pretrained/" \
--ser_model_type "LayoutXLM" \
--eval_data_dir "XFUND/zh_val/image" \
--eval_label_path "XFUND/zh_val/xfun_normalize_val.json" \
--per_gpu_eval_batch_size 8 \
--num_workers 8 \
--output_dir "output/ser/" \
--seed 2048
```
最终会打印出`precision`, `recall`, `f1`等指标
* 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
python3.7 infer_ser.py \
--model_name_or_path "./PP-Layout_v1.0_ser_pretrained/" \
--output_dir "output_res/" \
--model_name_or_path "PP-Layout_v1.0_ser_pretrained/" \
--ser_model_type "LayoutXLM" \
--output_dir "output/ser/" \
--infer_imgs "XFUND/zh_val/image/" \
--ocr_json_path "XFUND/zh_val/xfun_normalize_val.json"
```
......@@ -169,9 +207,10 @@ python3.7 infer_ser.py \
```shell
export CUDA_VISIBLE_DEVICES=0
python3.7 infer_ser_e2e.py \
--model_name_or_path "./output/PP-Layout_v1.0_ser_pretrained/" \
--model_name_or_path "PP-Layout_v1.0_ser_pretrained/" \
--ser_model_type "LayoutXLM" \
--max_seq_length 512 \
--output_dir "output_res_e2e/" \
--output_dir "output/ser_e2e/" \
--infer_imgs "images/input/zh_val_0.jpg"
```
......@@ -188,6 +227,7 @@ python3.7 helper/eval_with_label_end2end.py --gt_json_path XFUND/zh_val/xfun_nor
* 启动训练
```shell
export CUDA_VISIBLE_DEVICES=0
python3 train_re.py \
--model_name_or_path "layoutxlm-base-uncased" \
--train_data_dir "XFUND/zh_train/image" \
......@@ -195,32 +235,74 @@ python3 train_re.py \
--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 \
--num_train_epochs 200 \
--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 \
--num_workers 8 \
--evaluate_during_training \
--seed 2048
```
* 恢复训练
```shell
export CUDA_VISIBLE_DEVICES=0
python3 train_re.py \
--model_name_or_path "model_path" \
--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 \
--output_dir "output/re/" \
--learning_rate 5e-5 \
--warmup_steps 50 \
--per_gpu_train_batch_size 8 \
--per_gpu_eval_batch_size 8 \
--num_workers 8 \
--evaluate_during_training \
--seed 2048 \
--resume
```
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。
* 评估
```shell
export CUDA_VISIBLE_DEVICES=0
python3 eval_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/re/" \
--per_gpu_eval_batch_size 8 \
--num_workers 8 \
--seed 2048
```
最终会打印出`precision`, `recall`, `f1`等指标
* 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/" \
--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" \
--output_dir "output/re/" \
--per_gpu_eval_batch_size 1 \
--seed 2048
```
......@@ -231,11 +313,12 @@ python3 infer_re.py \
```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/" \
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/" \
--ser_model_type "LayoutXLM" \
--max_seq_length 512 \
--output_dir "output_ser_re_e2e_train/" \
--output_dir "output/ser_re_e2e/" \
--infer_imgs "images/input/zh_val_21.jpg"
```
......
ppstructure/vqa/eval_re.py 0 → 100644
浏览文件 @ 86b90aa9
# 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 os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
import paddle
from paddlenlp.transformers import LayoutXLMTokenizer, LayoutXLMModel, LayoutXLMForRelationExtraction
from xfun import XFUNDataset
from utils import parse_args, get_bio_label_maps, print_arguments
from data_collator import DataCollator
from metric import re_score
from ppocr.utils.logging import get_logger
def cal_metric(re_preds, re_labels, entities):
gt_relations = []
for b in range(len(re_labels)):
rel_sent = []
for head, tail in zip(re_labels[b]["head"], re_labels[b]["tail"]):
rel = {}
rel["head_id"] = head
rel["head"] = (entities[b]["start"][rel["head_id"]],
entities[b]["end"][rel["head_id"]])
rel["head_type"] = entities[b]["label"][rel["head_id"]]
rel["tail_id"] = tail
rel["tail"] = (entities[b]["start"][rel["tail_id"]],
entities[b]["end"][rel["tail_id"]])
rel["tail_type"] = entities[b]["label"][rel["tail_id"]]
rel["type"] = 1
rel_sent.append(rel)
gt_relations.append(rel_sent)
re_metrics = re_score(re_preds, gt_relations, mode="boundaries")
return re_metrics
def evaluate(model, eval_dataloader, logger, prefix=""):
# Eval!
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = {}".format(len(eval_dataloader.dataset)))
re_preds = []
re_labels = []
entities = []
eval_loss = 0.0
model.eval()
for idx, batch in enumerate(eval_dataloader):
with paddle.no_grad():
outputs = model(**batch)
loss = outputs['loss'].mean().item()
if paddle.distributed.get_rank() == 0:
logger.info("[Eval] process: {}/{}, loss: {:.5f}".format(
idx, len(eval_dataloader), loss))
eval_loss += loss
re_preds.extend(outputs['pred_relations'])
re_labels.extend(batch['relations'])
entities.extend(batch['entities'])
re_metrics = cal_metric(re_preds, re_labels, entities)
re_metrics = {
"precision": re_metrics["ALL"]["p"],
"recall": re_metrics["ALL"]["r"],
"f1": re_metrics["ALL"]["f1"],
}
model.train()
return re_metrics
def eval(args):
logger = get_logger()
label2id_map, id2label_map = get_bio_label_maps(args.label_map_path)
pad_token_label_id = paddle.nn.CrossEntropyLoss().ignore_index
tokenizer = LayoutXLMTokenizer.from_pretrained(args.model_name_or_path)
model = LayoutXLMForRelationExtraction.from_pretrained(
args.model_name_or_path)
eval_dataset = XFUNDataset(
tokenizer,
data_dir=args.eval_data_dir,
label_path=args.eval_label_path,
label2id_map=label2id_map,
img_size=(224, 224),
max_seq_len=args.max_seq_length,
pad_token_label_id=pad_token_label_id,
contains_re=True,
add_special_ids=False,
return_attention_mask=True,
load_mode='all')
eval_dataloader = paddle.io.DataLoader(
eval_dataset,
batch_size=args.per_gpu_eval_batch_size,
num_workers=args.num_workers,
shuffle=False,
collate_fn=DataCollator())
results = evaluate(model, eval_dataloader, logger)
logger.info("eval results: {}".format(results))
if __name__ == "__main__":
args = parse_args()
eval(args)
ppstructure/vqa/eval_ser.py 0 → 100644
浏览文件 @ 86b90aa9
# 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 os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
import random
import time
import copy
import logging
import argparse
import paddle
import numpy as np
from seqeval.metrics import classification_report, f1_score, precision_score, recall_score
from paddlenlp.transformers import LayoutXLMModel, LayoutXLMTokenizer, LayoutXLMForTokenClassification
from paddlenlp.transformers import LayoutLMModel, LayoutLMTokenizer, LayoutLMForTokenClassification
from xfun import XFUNDataset
from losses import SERLoss
from utils import parse_args, get_bio_label_maps, print_arguments
from ppocr.utils.logging import get_logger
MODELS = {
'LayoutXLM':
(LayoutXLMTokenizer, LayoutXLMModel, LayoutXLMForTokenClassification),
'LayoutLM':
(LayoutLMTokenizer, LayoutLMModel, LayoutLMForTokenClassification)
}
def eval(args):
logger = get_logger()
print_arguments(args, logger)
label2id_map, id2label_map = get_bio_label_maps(args.label_map_path)
pad_token_label_id = paddle.nn.CrossEntropyLoss().ignore_index
tokenizer_class, base_model_class, model_class = MODELS[args.ser_model_type]
tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
model = model_class.from_pretrained(args.model_name_or_path)
eval_dataset = XFUNDataset(
tokenizer,
data_dir=args.eval_data_dir,
label_path=args.eval_label_path,
label2id_map=label2id_map,
img_size=(224, 224),
pad_token_label_id=pad_token_label_id,
contains_re=False,
add_special_ids=False,
return_attention_mask=True,
load_mode='all')
eval_dataloader = paddle.io.DataLoader(
eval_dataset,
batch_size=args.per_gpu_eval_batch_size,
num_workers=args.num_workers,
use_shared_memory=True,
collate_fn=None, )
loss_class = SERLoss(len(label2id_map))
results, _ = evaluate(args, model, tokenizer, loss_class, eval_dataloader,
label2id_map, id2label_map, pad_token_label_id,
logger)
logger.info(results)
def evaluate(args,
model,
tokenizer,
loss_class,
eval_dataloader,
label2id_map,
id2label_map,
pad_token_label_id,
logger,
prefix=""):
eval_loss = 0.0
nb_eval_steps = 0
preds = None
out_label_ids = None
model.eval()
for idx, batch in enumerate(eval_dataloader):
with paddle.no_grad():
if args.ser_model_type == 'LayoutLM':
if 'image' in batch:
batch.pop('image')
labels = batch.pop('labels')
outputs = model(**batch)
if args.ser_model_type == 'LayoutXLM':
outputs = outputs[0]
loss = loss_class(labels, outputs, batch['attention_mask'])
loss = loss.mean()
if paddle.distributed.get_rank() == 0:
logger.info("[Eval]process: {}/{}, loss: {:.5f}".format(
idx, len(eval_dataloader), loss.numpy()[0]))
eval_loss += loss.item()
nb_eval_steps += 1
if preds is None:
preds = outputs.numpy()
out_label_ids = labels.numpy()
else:
preds = np.append(preds, outputs.numpy(), axis=0)
out_label_ids = np.append(out_label_ids, labels.numpy(), axis=0)
eval_loss = eval_loss / nb_eval_steps
preds = np.argmax(preds, axis=2)
# label_map = {i: label.upper() for i, label in enumerate(labels)}
out_label_list = [[] for _ in range(out_label_ids.shape[0])]
preds_list = [[] for _ in range(out_label_ids.shape[0])]
for i in range(out_label_ids.shape[0]):
for j in range(out_label_ids.shape[1]):
if out_label_ids[i, j] != pad_token_label_id:
out_label_list[i].append(id2label_map[out_label_ids[i][j]])
preds_list[i].append(id2label_map[preds[i][j]])
results = {
"loss": eval_loss,
"precision": precision_score(out_label_list, preds_list),
"recall": recall_score(out_label_list, preds_list),
"f1": f1_score(out_label_list, preds_list),
}
with open(
os.path.join(args.output_dir, "test_gt.txt"), "w",
encoding='utf-8') as fout:
for lbl in out_label_list:
for l in lbl:
fout.write(l + "\t")
fout.write("\n")
with open(
os.path.join(args.output_dir, "test_pred.txt"), "w",
encoding='utf-8') as fout:
for lbl in preds_list:
for l in lbl:
fout.write(l + "\t")
fout.write("\n")
report = classification_report(out_label_list, preds_list)
logger.info("\n" + report)
logger.info("***** Eval results %s *****", prefix)
for key in sorted(results.keys()):
logger.info(" %s = %s", key, str(results[key]))
model.train()
return results, preds_list
if __name__ == "__main__":
args = parse_args()
eval(args)
ppstructure/vqa/helper/eval_with_label_end2end.py
浏览文件 @ 86b90aa9
......@@ -15,13 +15,12 @@
import os
import re
import sys
# import Polygon
import shapely
from shapely.geometry import Polygon
import numpy as np
from collections import defaultdict
import operator
import editdistance
import Levenshtein
import argparse
import json
import copy
......@@ -38,7 +37,7 @@ def parse_ser_results_fp(fp, fp_type="gt", ignore_background=True):
assert fp_type in ["gt", "pred"]
key = "label" if fp_type == "gt" else "pred"
res_dict = dict()
with open(fp, "r") as fin:
with open(fp, "r", encoding='utf-8') as fin:
lines = fin.readlines()
for _, line in enumerate(lines):
......@@ -95,7 +94,7 @@ def ed(args, str1, str2):
if args.ignore_case:
str1 = str1.lower()
str2 = str2.lower()
return editdistance.eval(str1, str2)
return Levenshtein.distance(str1, str2)
def convert_bbox_to_polygon(bbox):
......@@ -115,8 +114,6 @@ def eval_e2e(args):
# pred
dt_results = parse_ser_results_fp(args.pred_json_path, "pred",
args.ignore_background)
assert set(gt_results.keys()) == set(dt_results.keys())
iou_thresh = args.iou_thres
num_gt_chars = 0
gt_count = 0
......@@ -124,7 +121,7 @@ def eval_e2e(args):
hit = 0
ed_sum = 0
for img_name in gt_results:
for img_name in dt_results:
gt_info = gt_results[img_name]
gt_count += len(gt_info)
......
ppstructure/vqa/helper/trans_xfun_data.py
浏览文件 @ 86b90aa9
......@@ -16,13 +16,13 @@ import json
def transfer_xfun_data(json_path=None, output_file=None):
with open(json_path, "r") as fin:
with open(json_path, "r", encoding='utf-8') as fin:
lines = fin.readlines()
json_info = json.loads(lines[0])
documents = json_info["documents"]
label_info = {}
with open(output_file, "w") as fout:
with open(output_file, "w", encoding='utf-8') as fout:
for idx, document in enumerate(documents):
img_info = document["img"]
document = document["document"]
......
ppstructure/vqa/infer.sh 0 → 100644
浏览文件 @ 86b90aa9
export CUDA_VISIBLE_DEVICES=6
# python3.7 infer_ser_e2e.py \
# --model_name_or_path "output/ser_distributed/best_model" \
# --max_seq_length 512 \
# --output_dir "output_res_e2e/" \
# --infer_imgs "/ssd1/zhoujun20/VQA/data/XFUN_v1.0_data/zh.val/zh_val_0.jpg"
# python3.7 infer_ser_re_e2e.py \
# --model_name_or_path "output/ser_distributed/best_model" \
# --re_model_name_or_path "output/re_test/best_model" \
# --max_seq_length 512 \
# --output_dir "output_ser_re_e2e_train/" \
# --infer_imgs "images/input/zh_val_21.jpg"
# python3.7 infer_ser.py \
# --model_name_or_path "output/ser_LayoutLM/best_model" \
# --ser_model_type "LayoutLM" \
# --output_dir "ser_LayoutLM/" \
# --infer_imgs "images/input/zh_val_21.jpg" \
# --ocr_json_path "/ssd1/zhoujun20/VQA/data/XFUN_v1.0_data/xfun_normalize_val.json"
python3.7 infer_ser.py \
--model_name_or_path "output/ser_new/best_model" \
--ser_model_type "LayoutXLM" \
--output_dir "ser_new/" \
--infer_imgs "images/input/zh_val_21.jpg" \
--ocr_json_path "/ssd1/zhoujun20/VQA/data/XFUN_v1.0_data/xfun_normalize_val.json"
# python3.7 infer_ser_e2e.py \
# --model_name_or_path "output/ser_new/best_model" \
# --ser_model_type "LayoutXLM" \
# --max_seq_length 512 \
# --output_dir "output/ser_new/" \
# --infer_imgs "images/input/zh_val_0.jpg"
# python3.7 infer_ser_e2e.py \
# --model_name_or_path "output/ser_LayoutLM/best_model" \
# --ser_model_type "LayoutLM" \
# --max_seq_length 512 \
# --output_dir "output/ser_LayoutLM/" \
# --infer_imgs "images/input/zh_val_0.jpg"
# python3 infer_re.py \
# --model_name_or_path "/ssd1/zhoujun20/VQA/PaddleOCR/ppstructure/vqa/output/re_test/best_model/" \
# --max_seq_length 512 \
# --eval_data_dir "/ssd1/zhoujun20/VQA/data/XFUN_v1.0_data/zh.val" \
# --eval_label_path "/ssd1/zhoujun20/VQA/data/XFUN_v1.0_data/xfun_normalize_val.json" \
# --label_map_path 'labels/labels_ser.txt' \
# --output_dir "output_res" \
# --per_gpu_eval_batch_size 1 \
# --seed 2048
# python3.7 infer_ser_re_e2e.py \
# --model_name_or_path "output/ser_LayoutLM/best_model" \
# --ser_model_type "LayoutLM" \
# --re_model_name_or_path "output/re_new/best_model" \
# --max_seq_length 512 \
# --output_dir "output_ser_re_e2e/" \
# --infer_imgs "images/input/zh_val_21.jpg"
\ No newline at end of file
ppstructure/vqa/infer_re.py
浏览文件 @ 86b90aa9
......@@ -56,15 +56,19 @@ def infer(args):
ocr_info_list = load_ocr(args.eval_data_dir, args.eval_label_path)
for idx, batch in enumerate(eval_dataloader):
logger.info("[Infer] process: {}/{}".format(idx, len(eval_dataloader)))
with paddle.no_grad():
outputs = model(**batch)
pred_relations = outputs['pred_relations']
ocr_info = ocr_info_list[idx]
image_path = ocr_info['image_path']
ocr_info = ocr_info['ocr_info']
save_img_path = os.path.join(
args.output_dir,
os.path.splitext(os.path.basename(image_path))[0] + "_re.jpg")
logger.info("[Infer] process: {}/{}, save result to {}".format(
idx, len(eval_dataloader), save_img_path))
with paddle.no_grad():
outputs = model(**batch)
pred_relations = outputs['pred_relations']
# 根据entity里的信息，做token解码后去过滤不要的ocr_info
ocr_info = filter_bg_by_txt(ocr_info, batch, tokenizer)
......@@ -85,14 +89,13 @@ def infer(args):
img = cv2.imread(image_path)
img_show = draw_re_results(img, result)
save_path = os.path.join(args.output_dir, os.path.basename(image_path))
cv2.imwrite(save_path, img_show)
cv2.imwrite(save_img_path, img_show)
def load_ocr(img_folder, json_path):
import json
d = []
with open(json_path, "r") as fin:
with open(json_path, "r", encoding='utf-8') as fin:
lines = fin.readlines()
for line in lines:
image_name, info_str = line.split("\t")
......
ppstructure/vqa/infer_ser.py
浏览文件 @ 86b90aa9
......@@ -24,6 +24,14 @@ import paddle
# relative reference
from utils import parse_args, get_image_file_list, draw_ser_results, get_bio_label_maps
from paddlenlp.transformers import LayoutXLMModel, LayoutXLMTokenizer, LayoutXLMForTokenClassification
from paddlenlp.transformers import LayoutLMModel, LayoutLMTokenizer, LayoutLMForTokenClassification
MODELS = {
'LayoutXLM':
(LayoutXLMTokenizer, LayoutXLMModel, LayoutXLMForTokenClassification),
'LayoutLM':
(LayoutLMTokenizer, LayoutLMModel, LayoutLMForTokenClassification)
}
def pad_sentences(tokenizer,
......@@ -59,7 +67,8 @@ def pad_sentences(tokenizer,
encoded_inputs["bbox"] = encoded_inputs["bbox"] + [[0, 0, 0, 0]
] * difference
else:
assert False, f"padding_side of tokenizer just supports [\"right\"] but got {tokenizer.padding_side}"
assert False, "padding_side of tokenizer just supports [\"right\"] but got {}".format(
tokenizer.padding_side)
else:
if return_attention_mask:
encoded_inputs["attention_mask"] = [1] * len(encoded_inputs[
......@@ -216,15 +225,15 @@ def infer(args):
os.makedirs(args.output_dir, exist_ok=True)
# init token and model
tokenizer = LayoutXLMTokenizer.from_pretrained(args.model_name_or_path)
# model = LayoutXLMModel.from_pretrained(args.model_name_or_path)
model = LayoutXLMForTokenClassification.from_pretrained(
args.model_name_or_path)
tokenizer_class, base_model_class, model_class = MODELS[args.ser_model_type]
tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
model = model_class.from_pretrained(args.model_name_or_path)
model.eval()
# load ocr results json
ocr_results = dict()
with open(args.ocr_json_path, "r") as fin:
with open(args.ocr_json_path, "r", encoding='utf-8') as fin:
lines = fin.readlines()
for line in lines:
img_name, json_info = line.split("\t")
......@@ -234,9 +243,15 @@ def infer(args):
infer_imgs = get_image_file_list(args.infer_imgs)
# loop for infer
with open(os.path.join(args.output_dir, "infer_results.txt"), "w") as fout:
with open(
os.path.join(args.output_dir, "infer_results.txt"),
"w",
encoding='utf-8') as fout:
for idx, img_path in enumerate(infer_imgs):
print("process: [{}/{}]".format(idx, len(infer_imgs), img_path))
save_img_path = os.path.join(args.output_dir,
os.path.basename(img_path))
print("process: [{}/{}], save result to {}".format(
idx, len(infer_imgs), save_img_path))
img = cv2.imread(img_path)
......@@ -246,15 +261,21 @@ def infer(args):
ori_img=img,
ocr_info=ocr_info,
max_seq_len=args.max_seq_length)
if args.ser_model_type == 'LayoutLM':
preds = model(
input_ids=inputs["input_ids"],
bbox=inputs["bbox"],
token_type_ids=inputs["token_type_ids"],
attention_mask=inputs["attention_mask"])
elif args.ser_model_type == 'LayoutXLM':
preds = model(
input_ids=inputs["input_ids"],
bbox=inputs["bbox"],
image=inputs["image"],
token_type_ids=inputs["token_type_ids"],
attention_mask=inputs["attention_mask"])
preds = preds[0]
outputs = model(
input_ids=inputs["input_ids"],
bbox=inputs["bbox"],
image=inputs["image"],
token_type_ids=inputs["token_type_ids"],
attention_mask=inputs["attention_mask"])
preds = outputs[0]
preds = postprocess(inputs["attention_mask"], preds,
args.label_map_path)
ocr_info = merge_preds_list_with_ocr_info(
......@@ -267,9 +288,7 @@ def infer(args):
}, ensure_ascii=False) + "\n")
img_res = draw_ser_results(img, ocr_info)
cv2.imwrite(
os.path.join(args.output_dir, os.path.basename(img_path)),
img_res)
cv2.imwrite(save_img_path, img_res)
return
......
ppstructure/vqa/infer_ser_e2e.py
浏览文件 @ 86b90aa9
......@@ -22,14 +22,20 @@ from PIL import Image
import paddle
from paddlenlp.transformers import LayoutXLMModel, LayoutXLMTokenizer, LayoutXLMForTokenClassification
from paddleocr import PaddleOCR
from paddlenlp.transformers import LayoutLMModel, LayoutLMTokenizer, LayoutLMForTokenClassification
# relative reference
from utils import parse_args, get_image_file_list, draw_ser_results, get_bio_label_maps
from utils import pad_sentences, split_page, preprocess, postprocess, merge_preds_list_with_ocr_info
MODELS = {
'LayoutXLM':
(LayoutXLMTokenizer, LayoutXLMModel, LayoutXLMForTokenClassification),
'LayoutLM':
(LayoutLMTokenizer, LayoutLMModel, LayoutLMForTokenClassification)
}
def trans_poly_to_bbox(poly):
x1 = np.min([p[0] for p in poly])
......@@ -52,19 +58,23 @@ def parse_ocr_info_for_ser(ocr_result):
class SerPredictor(object):
def __init__(self, args):
self.args = args
self.max_seq_length = args.max_seq_length
# init ser token and model
self.tokenizer = LayoutXLMTokenizer.from_pretrained(
args.model_name_or_path)
self.model = LayoutXLMForTokenClassification.from_pretrained(
tokenizer_class, base_model_class, model_class = MODELS[
args.ser_model_type]
self.tokenizer = tokenizer_class.from_pretrained(
args.model_name_or_path)
self.model = model_class.from_pretrained(args.model_name_or_path)
self.model.eval()
# init ocr_engine
from paddleocr import PaddleOCR
self.ocr_engine = PaddleOCR(
rec_model_dir=args.ocr_rec_model_dir,
det_model_dir=args.ocr_det_model_dir,
rec_model_dir=args.rec_model_dir,
det_model_dir=args.det_model_dir,
use_angle_cls=False,
show_log=False)
# init dict
......@@ -88,14 +98,21 @@ class SerPredictor(object):
ocr_info=ocr_info,
max_seq_len=self.max_seq_length)
outputs = self.model(
input_ids=inputs["input_ids"],
bbox=inputs["bbox"],
image=inputs["image"],
token_type_ids=inputs["token_type_ids"],
attention_mask=inputs["attention_mask"])
if self.args.ser_model_type == 'LayoutLM':
preds = self.model(
input_ids=inputs["input_ids"],
bbox=inputs["bbox"],
token_type_ids=inputs["token_type_ids"],
attention_mask=inputs["attention_mask"])
elif self.args.ser_model_type == 'LayoutXLM':
preds = self.model(
input_ids=inputs["input_ids"],
bbox=inputs["bbox"],
image=inputs["image"],
token_type_ids=inputs["token_type_ids"],
attention_mask=inputs["attention_mask"])
preds = preds[0]
preds = outputs[0]
preds = postprocess(inputs["attention_mask"], preds, self.id2label_map)
ocr_info = merge_preds_list_with_ocr_info(
ocr_info, inputs["segment_offset_id"], preds,
......@@ -112,9 +129,16 @@ if __name__ == "__main__":
# loop for infer
ser_engine = SerPredictor(args)
with open(os.path.join(args.output_dir, "infer_results.txt"), "w") as fout:
with open(
os.path.join(args.output_dir, "infer_results.txt"),
"w",
encoding='utf-8') as fout:
for idx, img_path in enumerate(infer_imgs):
print("process: [{}/{}], {}".format(idx, len(infer_imgs), img_path))
save_img_path = os.path.join(
args.output_dir,
os.path.splitext(os.path.basename(img_path))[0] + "_ser.jpg")
print("process: [{}/{}], save result to {}".format(
idx, len(infer_imgs), save_img_path))
img = cv2.imread(img_path)
......@@ -125,7 +149,4 @@ if __name__ == "__main__":
}, ensure_ascii=False) + "\n")
img_res = draw_ser_results(img, result)
cv2.imwrite(
os.path.join(args.output_dir,
os.path.splitext(os.path.basename(img_path))[0] +
"_ser.jpg"), img_res)
cv2.imwrite(save_img_path, img_res)
ppstructure/vqa/infer_ser_re_e2e.py
浏览文件 @ 86b90aa9
......@@ -112,9 +112,16 @@ if __name__ == "__main__":
# loop for infer
ser_re_engine = SerReSystem(args)
with open(os.path.join(args.output_dir, "infer_results.txt"), "w") as fout:
with open(
os.path.join(args.output_dir, "infer_results.txt"),
"w",
encoding='utf-8') as fout:
for idx, img_path in enumerate(infer_imgs):
print("process: [{}/{}], {}".format(idx, len(infer_imgs), img_path))
save_img_path = os.path.join(
args.output_dir,
os.path.splitext(os.path.basename(img_path))[0] + "_re.jpg")
print("process: [{}/{}], save result to {}".format(
idx, len(infer_imgs), save_img_path))
img = cv2.imread(img_path)
......@@ -125,7 +132,4 @@ if __name__ == "__main__":
}, ensure_ascii=False) + "\n")
img_res = draw_re_results(img, result)
cv2.imwrite(
os.path.join(args.output_dir,
os.path.splitext(os.path.basename(img_path))[0] +
"_re.jpg"), img_res)
cv2.imwrite(save_img_path, img_res)
ppstructure/vqa/losses.py 0 → 100644
浏览文件 @ 86b90aa9
# 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.
from paddle import nn
class SERLoss(nn.Layer):
def __init__(self, num_classes):
super().__init__()
self.loss_class = nn.CrossEntropyLoss()
self.num_classes = num_classes
self.ignore_index = self.loss_class.ignore_index
def forward(self, labels, outputs, attention_mask):
if attention_mask is not None:
active_loss = attention_mask.reshape([-1, ]) == 1
active_outputs = outputs.reshape(
[-1, self.num_classes])[active_loss]
active_labels = labels.reshape([-1, ])[active_loss]
loss = self.loss_class(active_outputs, active_labels)
else:
loss = self.loss_class(
outputs.reshape([-1, self.num_classes]), labels.reshape([-1, ]))
return loss
ppstructure/vqa/requirements.txt
浏览文件 @ 86b90aa9
sentencepiece
yacs
seqeval
\ No newline at end of file
ppstructure/vqa/train_re.py
浏览文件 @ 86b90aa9
......@@ -20,82 +20,25 @@ sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
import random
import time
import numpy as np
import paddle
from paddlenlp.transformers import LayoutXLMTokenizer, LayoutXLMModel, LayoutXLMForRelationExtraction
from xfun import XFUNDataset
from utils import parse_args, get_bio_label_maps, print_arguments
from utils import parse_args, get_bio_label_maps, print_arguments, set_seed
from data_collator import DataCollator
from metric import re_score
from eval_re import evaluate
from ppocr.utils.logging import get_logger
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
paddle.seed(seed)
def cal_metric(re_preds, re_labels, entities):
gt_relations = []
for b in range(len(re_labels)):
rel_sent = []
for head, tail in zip(re_labels[b]["head"], re_labels[b]["tail"]):
rel = {}
rel["head_id"] = head
rel["head"] = (entities[b]["start"][rel["head_id"]],
entities[b]["end"][rel["head_id"]])
rel["head_type"] = entities[b]["label"][rel["head_id"]]
rel["tail_id"] = tail
rel["tail"] = (entities[b]["start"][rel["tail_id"]],
entities[b]["end"][rel["tail_id"]])
rel["tail_type"] = entities[b]["label"][rel["tail_id"]]
rel["type"] = 1
rel_sent.append(rel)
gt_relations.append(rel_sent)
re_metrics = re_score(re_preds, gt_relations, mode="boundaries")
return re_metrics
def evaluate(model, eval_dataloader, logger, prefix=""):
# Eval!
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = {}".format(len(eval_dataloader.dataset)))
re_preds = []
re_labels = []
entities = []
eval_loss = 0.0
model.eval()
for idx, batch in enumerate(eval_dataloader):
with paddle.no_grad():
outputs = model(**batch)
loss = outputs['loss'].mean().item()
if paddle.distributed.get_rank() == 0:
logger.info("[Eval] process: {}/{}, loss: {:.5f}".format(
idx, len(eval_dataloader), loss))
eval_loss += loss
re_preds.extend(outputs['pred_relations'])
re_labels.extend(batch['relations'])
entities.extend(batch['entities'])
re_metrics = cal_metric(re_preds, re_labels, entities)
re_metrics = {
"precision": re_metrics["ALL"]["p"],
"recall": re_metrics["ALL"]["r"],
"f1": re_metrics["ALL"]["f1"],
}
model.train()
return re_metrics
def train(args):
logger = get_logger(log_file=os.path.join(args.output_dir, "train.log"))
rank = paddle.distributed.get_rank()
distributed = paddle.distributed.get_world_size() > 1
print_arguments(args, logger)
# Added here for reproducibility (even between python 2 and 3)
......@@ -105,17 +48,22 @@ def train(args):
pad_token_label_id = paddle.nn.CrossEntropyLoss().ignore_index
# dist mode
if paddle.distributed.get_world_size() > 1:
if distributed:
paddle.distributed.init_parallel_env()
tokenizer = LayoutXLMTokenizer.from_pretrained(args.model_name_or_path)
model = LayoutXLMModel.from_pretrained(args.model_name_or_path)
model = LayoutXLMForRelationExtraction(model, dropout=None)
if not args.resume:
model = LayoutXLMModel.from_pretrained(args.model_name_or_path)
model = LayoutXLMForRelationExtraction(model, dropout=None)
logger.info('train from scratch')
else:
logger.info('resume from {}'.format(args.model_name_or_path))
model = LayoutXLMForRelationExtraction.from_pretrained(
args.model_name_or_path)
# dist mode
if paddle.distributed.get_world_size() > 1:
model = paddle.distributed.DataParallel(model)
if distributed:
model = paddle.DataParallel(model)
train_dataset = XFUNDataset(
tokenizer,
......@@ -145,19 +93,18 @@ def train(args):
train_sampler = paddle.io.DistributedBatchSampler(
train_dataset, batch_size=args.per_gpu_train_batch_size, shuffle=True)
args.train_batch_size = args.per_gpu_train_batch_size * \
max(1, paddle.distributed.get_world_size())
train_dataloader = paddle.io.DataLoader(
train_dataset,
batch_sampler=train_sampler,
num_workers=8,
num_workers=args.num_workers,
use_shared_memory=True,
collate_fn=DataCollator())
eval_dataloader = paddle.io.DataLoader(
eval_dataset,
batch_size=args.per_gpu_eval_batch_size,
num_workers=8,
num_workers=args.num_workers,
shuffle=False,
collate_fn=DataCollator())
......@@ -191,7 +138,8 @@ def train(args):
args.per_gpu_train_batch_size))
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = {}".
format(args.train_batch_size * paddle.distributed.get_world_size()))
format(args.per_gpu_train_batch_size *
paddle.distributed.get_world_size()))
logger.info(" Total optimization steps = {}".format(t_total))
global_step = 0
......@@ -200,58 +148,78 @@ def train(args):
best_metirc = {'f1': 0}
model.train()
train_reader_cost = 0.0
train_run_cost = 0.0
total_samples = 0
reader_start = time.time()
print_step = 1
for epoch in range(int(args.num_train_epochs)):
for step, batch in enumerate(train_dataloader):
train_reader_cost += time.time() - reader_start
train_start = time.time()
outputs = model(**batch)
train_run_cost += time.time() - train_start
# model outputs are always tuple in ppnlp (see doc)
loss = outputs['loss']
loss = loss.mean()
logger.info(
"epoch: [{}/{}], iter: [{}/{}], global_step:{}, train loss: {}, lr: {}".
format(epoch, args.num_train_epochs, step, train_dataloader_len,
global_step, np.mean(loss.numpy()), optimizer.get_lr()))
loss.backward()
optimizer.step()
optimizer.clear_grad()
# lr_scheduler.step() # Update learning rate schedule
global_step += 1
if (paddle.distributed.get_rank() == 0 and args.eval_steps > 0 and
global_step % args.eval_steps == 0):
total_samples += batch['image'].shape[0]
if rank == 0 and step % print_step == 0:
logger.info(
"epoch: [{}/{}], iter: [{}/{}], global_step:{}, train loss: {:.6f}, lr: {:.6f}, avg_reader_cost: {:.5f} sec, avg_batch_cost: {:.5f} sec, avg_samples: {:.5f}, ips: {:.5f} images/sec".
format(epoch, args.num_train_epochs, step,
train_dataloader_len, global_step,
np.mean(loss.numpy()),
optimizer.get_lr(), train_reader_cost / print_step, (
train_reader_cost + train_run_cost) / print_step,
total_samples / print_step, total_samples / (
train_reader_cost + train_run_cost)))
train_reader_cost = 0.0
train_run_cost = 0.0
total_samples = 0
if rank == 0 and args.eval_steps > 0 and global_step % args.eval_steps == 0 and args.evaluate_during_training:
# Log metrics
if (paddle.distributed.get_rank() == 0 and args.
evaluate_during_training): # Only evaluate when single GPU otherwise metrics may not average well
results = evaluate(model, eval_dataloader, logger)
if results['f1'] > best_metirc['f1']:
best_metirc = results
output_dir = os.path.join(args.output_dir,
"checkpoint-best")
os.makedirs(output_dir, exist_ok=True)
# Only evaluate when single GPU otherwise metrics may not average well
results = evaluate(model, eval_dataloader, logger)
if results['f1'] >= best_metirc['f1']:
best_metirc = results
output_dir = os.path.join(args.output_dir, "best_model")
os.makedirs(output_dir, exist_ok=True)
if distributed:
model._layers.save_pretrained(output_dir)
else:
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
paddle.save(args,
os.path.join(output_dir,
"training_args.bin"))
logger.info("Saving model checkpoint to {}".format(
output_dir))
logger.info("eval results: {}".format(results))
logger.info("best_metirc: {}".format(best_metirc))
if (paddle.distributed.get_rank() == 0 and args.save_steps > 0 and
global_step % args.save_steps == 0):
# Save model checkpoint
output_dir = os.path.join(args.output_dir, "checkpoint-latest")
os.makedirs(output_dir, exist_ok=True)
if paddle.distributed.get_rank() == 0:
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
paddle.save(args,
os.path.join(output_dir, "training_args.bin"))
logger.info("Saving model checkpoint to {}".format(
output_dir))
logger.info("eval results: {}".format(results))
logger.info("best_metirc: {}".format(best_metirc))
reader_start = time.time()
if rank == 0:
# Save model checkpoint
output_dir = os.path.join(args.output_dir, "latest_model")
os.makedirs(output_dir, exist_ok=True)
if distributed:
model._layers.save_pretrained(output_dir)
else:
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
paddle.save(args, os.path.join(output_dir, "training_args.bin"))
logger.info("Saving model checkpoint to {}".format(output_dir))
logger.info("best_metirc: {}".format(best_metirc))
......
ppstructure/vqa/train_ser.py
浏览文件 @ 86b90aa9
......@@ -20,6 +20,7 @@ sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
import random
import time
import copy
import logging
......@@ -28,39 +29,52 @@ import paddle
import numpy as np
from seqeval.metrics import classification_report, f1_score, precision_score, recall_score
from paddlenlp.transformers import LayoutXLMModel, LayoutXLMTokenizer, LayoutXLMForTokenClassification
from xfun import XFUNDataset
from utils import parse_args
from utils import get_bio_label_maps
from utils import print_arguments
from paddlenlp.transformers import LayoutLMModel, LayoutLMTokenizer, LayoutLMForTokenClassification
from xfun import XFUNDataset
from utils import parse_args, get_bio_label_maps, print_arguments, set_seed
from eval_ser import evaluate
from losses import SERLoss
from ppocr.utils.logging import get_logger
def set_seed(args):
random.seed(args.seed)
np.random.seed(args.seed)
paddle.seed(args.seed)
MODELS = {
'LayoutXLM':
(LayoutXLMTokenizer, LayoutXLMModel, LayoutXLMForTokenClassification),
'LayoutLM':
(LayoutLMTokenizer, LayoutLMModel, LayoutLMForTokenClassification)
}
def train(args):
os.makedirs(args.output_dir, exist_ok=True)
rank = paddle.distributed.get_rank()
distributed = paddle.distributed.get_world_size() > 1
logger = get_logger(log_file=os.path.join(args.output_dir, "train.log"))
print_arguments(args, logger)
label2id_map, id2label_map = get_bio_label_maps(args.label_map_path)
pad_token_label_id = paddle.nn.CrossEntropyLoss().ignore_index
loss_class = SERLoss(len(label2id_map))
pad_token_label_id = loss_class.ignore_index
# dist mode
if paddle.distributed.get_world_size() > 1:
if distributed:
paddle.distributed.init_parallel_env()
tokenizer = LayoutXLMTokenizer.from_pretrained(args.model_name_or_path)
base_model = LayoutXLMModel.from_pretrained(args.model_name_or_path)
model = LayoutXLMForTokenClassification(
base_model, num_classes=len(label2id_map), dropout=None)
tokenizer_class, base_model_class, model_class = MODELS[args.ser_model_type]
tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
if not args.resume:
base_model = base_model_class.from_pretrained(args.model_name_or_path)
model = model_class(
base_model, num_classes=len(label2id_map), dropout=None)
logger.info('train from scratch')
else:
logger.info('resume from {}'.format(args.model_name_or_path))
model = model_class.from_pretrained(args.model_name_or_path)
# dist mode
if paddle.distributed.get_world_size() > 1:
if distributed:
model = paddle.DataParallel(model)
train_dataset = XFUNDataset(
......@@ -74,17 +88,32 @@ def train(args):
add_special_ids=False,
return_attention_mask=True,
load_mode='all')
eval_dataset = XFUNDataset(
tokenizer,
data_dir=args.eval_data_dir,
label_path=args.eval_label_path,
label2id_map=label2id_map,
img_size=(224, 224),
pad_token_label_id=pad_token_label_id,
contains_re=False,
add_special_ids=False,
return_attention_mask=True,
load_mode='all')
train_sampler = paddle.io.DistributedBatchSampler(
train_dataset, batch_size=args.per_gpu_train_batch_size, shuffle=True)
args.train_batch_size = args.per_gpu_train_batch_size * max(
1, paddle.distributed.get_world_size())
train_dataloader = paddle.io.DataLoader(
train_dataset,
batch_sampler=train_sampler,
num_workers=0,
num_workers=args.num_workers,
use_shared_memory=True,
collate_fn=None, )
eval_dataloader = paddle.io.DataLoader(
eval_dataset,
batch_size=args.per_gpu_eval_batch_size,
num_workers=args.num_workers,
use_shared_memory=True,
collate_fn=None, )
......@@ -117,182 +146,103 @@ def train(args):
args.per_gpu_train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed) = %d",
args.train_batch_size * paddle.distributed.get_world_size(), )
args.per_gpu_train_batch_size * paddle.distributed.get_world_size(), )
logger.info(" Total optimization steps = %d", t_total)
global_step = 0
tr_loss = 0.0
set_seed(args)
set_seed(args.seed)
best_metrics = None
train_reader_cost = 0.0
train_run_cost = 0.0
total_samples = 0
reader_start = time.time()
print_step = 1
model.train()
for epoch_id in range(args.num_train_epochs):
for step, batch in enumerate(train_dataloader):
model.train()
train_reader_cost += time.time() - reader_start
if args.ser_model_type == 'LayoutLM':
if 'image' in batch:
batch.pop('image')
labels = batch.pop('labels')
train_start = time.time()
outputs = model(**batch)
train_run_cost += time.time() - train_start
if args.ser_model_type == 'LayoutXLM':
outputs = outputs[0]
loss = loss_class(labels, outputs, batch['attention_mask'])
# model outputs are always tuple in ppnlp (see doc)
loss = outputs[0]
loss = loss.mean()
logger.info(
"epoch: [{}/{}], iter: [{}/{}], global_step:{}, train loss: {}, lr: {}".
format(epoch_id, args.num_train_epochs, step,
len(train_dataloader), global_step,
loss.numpy()[0], lr_scheduler.get_lr()))
loss.backward()
tr_loss += loss.item()
optimizer.step()
lr_scheduler.step() # Update learning rate schedule
optimizer.clear_grad()
global_step += 1
if (paddle.distributed.get_rank() == 0 and args.eval_steps > 0 and
global_step % args.eval_steps == 0):
total_samples += batch['input_ids'].shape[0]
if rank == 0 and step % print_step == 0:
logger.info(
"epoch: [{}/{}], iter: [{}/{}], global_step:{}, train loss: {:.6f}, lr: {:.6f}, avg_reader_cost: {:.5f} sec, avg_batch_cost: {:.5f} sec, avg_samples: {:.5f}, ips: {:.5f} images/sec".
format(epoch_id, args.num_train_epochs, step,
len(train_dataloader), global_step,
loss.numpy()[0],
lr_scheduler.get_lr(), train_reader_cost /
print_step, (train_reader_cost + train_run_cost) /
print_step, total_samples / print_step, total_samples
/ (train_reader_cost + train_run_cost)))
train_reader_cost = 0.0
train_run_cost = 0.0
total_samples = 0
if rank == 0 and args.eval_steps > 0 and global_step % args.eval_steps == 0 and args.evaluate_during_training:
# Log metrics
# Only evaluate when single GPU otherwise metrics may not average well
if paddle.distributed.get_rank(
) == 0 and args.evaluate_during_training:
results, _ = evaluate(args, model, tokenizer, label2id_map,
id2label_map, pad_token_label_id,
logger)
if best_metrics is None or results["f1"] >= best_metrics[
"f1"]:
best_metrics = copy.deepcopy(results)
output_dir = os.path.join(args.output_dir, "best_model")
os.makedirs(output_dir, exist_ok=True)
if paddle.distributed.get_rank() == 0:
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
paddle.save(
args,
os.path.join(output_dir, "training_args.bin"))
logger.info("Saving model checkpoint to %s",
output_dir)
logger.info("[epoch {}/{}][iter: {}/{}] results: {}".format(
epoch_id, args.num_train_epochs, step,
len(train_dataloader), results))
if best_metrics is not None:
logger.info("best metrics: {}".format(best_metrics))
if paddle.distributed.get_rank(
) == 0 and args.save_steps > 0 and global_step % args.save_steps == 0:
# Save model checkpoint
output_dir = os.path.join(args.output_dir,
"checkpoint-{}".format(global_step))
os.makedirs(output_dir, exist_ok=True)
if paddle.distributed.get_rank() == 0:
model.save_pretrained(output_dir)
results, _ = evaluate(args, model, tokenizer, loss_class,
eval_dataloader, label2id_map,
id2label_map, pad_token_label_id, logger)
if best_metrics is None or results["f1"] >= best_metrics["f1"]:
best_metrics = copy.deepcopy(results)
output_dir = os.path.join(args.output_dir, "best_model")
os.makedirs(output_dir, exist_ok=True)
if distributed:
model._layers.save_pretrained(output_dir)
else:
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
paddle.save(args,
os.path.join(output_dir, "training_args.bin"))
logger.info("Saving model checkpoint to %s", output_dir)
logger.info("Saving model checkpoint to {}".format(
output_dir))
logger.info("[epoch {}/{}][iter: {}/{}] results: {}".format(
epoch_id, args.num_train_epochs, step,
len(train_dataloader), results))
if best_metrics is not None:
logger.info("best metrics: {}".format(best_metrics))
reader_start = time.time()
if rank == 0:
# Save model checkpoint
output_dir = os.path.join(args.output_dir, "latest_model")
os.makedirs(output_dir, exist_ok=True)
if distributed:
model._layers.save_pretrained(output_dir)
else:
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
paddle.save(args, os.path.join(output_dir, "training_args.bin"))
logger.info("Saving model checkpoint to {}".format(output_dir))
return global_step, tr_loss / global_step
def evaluate(args,
model,
tokenizer,
label2id_map,
id2label_map,
pad_token_label_id,
logger,
prefix=""):
eval_dataset = XFUNDataset(
tokenizer,
data_dir=args.eval_data_dir,
label_path=args.eval_label_path,
label2id_map=label2id_map,
img_size=(224, 224),
pad_token_label_id=pad_token_label_id,
contains_re=False,
add_special_ids=False,
return_attention_mask=True,
load_mode='all')
args.eval_batch_size = args.per_gpu_eval_batch_size * max(
1, paddle.distributed.get_world_size())
eval_dataloader = paddle.io.DataLoader(
eval_dataset,
batch_size=args.eval_batch_size,
num_workers=0,
use_shared_memory=True,
collate_fn=None, )
# Eval!
logger.info("***** Running evaluation %s *****", prefix)
logger.info(" Num examples = %d", len(eval_dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
eval_loss = 0.0
nb_eval_steps = 0
preds = None
out_label_ids = None
model.eval()
for idx, batch in enumerate(eval_dataloader):
with paddle.no_grad():
outputs = model(**batch)
tmp_eval_loss, logits = outputs[:2]
tmp_eval_loss = tmp_eval_loss.mean()
if paddle.distributed.get_rank() == 0:
logger.info("[Eval]process: {}/{}, loss: {:.5f}".format(
idx, len(eval_dataloader), tmp_eval_loss.numpy()[0]))
eval_loss += tmp_eval_loss.item()
nb_eval_steps += 1
if preds is None:
preds = logits.numpy()
out_label_ids = batch["labels"].numpy()
else:
preds = np.append(preds, logits.numpy(), axis=0)
out_label_ids = np.append(
out_label_ids, batch["labels"].numpy(), axis=0)
eval_loss = eval_loss / nb_eval_steps
preds = np.argmax(preds, axis=2)
# label_map = {i: label.upper() for i, label in enumerate(labels)}
out_label_list = [[] for _ in range(out_label_ids.shape[0])]
preds_list = [[] for _ in range(out_label_ids.shape[0])]
for i in range(out_label_ids.shape[0]):
for j in range(out_label_ids.shape[1]):
if out_label_ids[i, j] != pad_token_label_id:
out_label_list[i].append(id2label_map[out_label_ids[i][j]])
preds_list[i].append(id2label_map[preds[i][j]])
results = {
"loss": eval_loss,
"precision": precision_score(out_label_list, preds_list),
"recall": recall_score(out_label_list, preds_list),
"f1": f1_score(out_label_list, preds_list),
}
with open(os.path.join(args.output_dir, "test_gt.txt"), "w") as fout:
for lbl in out_label_list:
for l in lbl:
fout.write(l + "\t")
fout.write("\n")
with open(os.path.join(args.output_dir, "test_pred.txt"), "w") as fout:
for lbl in preds_list:
for l in lbl:
fout.write(l + "\t")
fout.write("\n")
report = classification_report(out_label_list, preds_list)
logger.info("\n" + report)
logger.info("***** Eval results %s *****", prefix)
for key in sorted(results.keys()):
logger.info(" %s = %s", key, str(results[key]))
return results, preds_list
if __name__ == "__main__":
args = parse_args()
train(args)
ppstructure/vqa/utils.py
浏览文件 @ 86b90aa9
......@@ -25,8 +25,14 @@ import paddle
from PIL import Image, ImageDraw, ImageFont
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
paddle.seed(seed)
def get_bio_label_maps(label_map_path):
with open(label_map_path, "r") as fin:
with open(label_map_path, "r", encoding='utf-8') as fin:
lines = fin.readlines()
lines = [line.strip() for line in lines]
if "O" not in lines:
......@@ -344,6 +350,8 @@ def parse_args():
# yapf: disable
parser.add_argument("--model_name_or_path",
default=None, type=str, required=True,)
parser.add_argument("--ser_model_type",
default='LayoutXLM', type=str)
parser.add_argument("--re_model_name_or_path",
default=None, type=str, required=False,)
parser.add_argument("--train_data_dir", default=None,
......@@ -357,6 +365,7 @@ def parse_args():
parser.add_argument("--output_dir", default=None, type=str, required=True,)
parser.add_argument("--max_seq_length", default=512, type=int,)
parser.add_argument("--evaluate_during_training", action="store_true",)
parser.add_argument("--num_workers", default=8, type=int,)
parser.add_argument("--per_gpu_train_batch_size", default=8,
type=int, help="Batch size per GPU/CPU for training.",)
parser.add_argument("--per_gpu_eval_batch_size", default=8,
......@@ -375,16 +384,15 @@ def parse_args():
help="Linear warmup over warmup_steps.",)
parser.add_argument("--eval_steps", type=int, default=10,
help="eval every X updates steps.",)
parser.add_argument("--save_steps", type=int, default=50,
help="Save checkpoint every X updates steps.",)
parser.add_argument("--seed", type=int, default=2048,
help="random seed for initialization",)
parser.add_argument("--ocr_rec_model_dir", default=None, type=str, )
parser.add_argument("--ocr_det_model_dir", default=None, type=str, )
parser.add_argument("--rec_model_dir", default=None, type=str, )
parser.add_argument("--det_model_dir", default=None, type=str, )
parser.add_argument(
"--label_map_path", default="./labels/labels_ser.txt", type=str, required=False, )
parser.add_argument("--infer_imgs", default=None, type=str, required=False)
parser.add_argument("--resume", action='store_true')
parser.add_argument("--ocr_json_path", default=None,
type=str, required=False, help="ocr prediction results")
# yapf: enable
......
ppstructure/vqa/xfun.py
浏览文件 @ 86b90aa9
......@@ -79,14 +79,36 @@ class XFUNDataset(Dataset):
self.entities_labels = {'HEADER': 0, 'QUESTION': 1, 'ANSWER': 2}
self.return_keys = {
'bbox': 'np',
'input_ids': 'np',
'labels': 'np',
'attention_mask': 'np',
'image': 'np',
'token_type_ids': 'np',
'entities': 'dict',
'relations': 'dict',
'bbox': {
'type': 'np',
'dtype': 'int64'
},
'input_ids': {
'type': 'np',
'dtype': 'int64'
},
'labels': {
'type': 'np',
'dtype': 'int64'
},
'attention_mask': {
'type': 'np',
'dtype': 'int64'
},
'image': {
'type': 'np',
'dtype': 'float32'
},
'token_type_ids': {
'type': 'np',
'dtype': 'int64'
},
'entities': {
'type': 'dict'
},
'relations': {
'type': 'dict'
}
}
if load_mode == "all":
......@@ -103,7 +125,7 @@ class XFUNDataset(Dataset):
return_special_tokens_mask=False):
# Padding
needs_to_be_padded = pad_to_max_seq_len and \
max_seq_len and len(encoded_inputs["input_ids"]) < max_seq_len
max_seq_len and len(encoded_inputs["input_ids"]) < max_seq_len
if needs_to_be_padded:
difference = max_seq_len - len(encoded_inputs["input_ids"])
......@@ -162,7 +184,7 @@ class XFUNDataset(Dataset):
return encoded_inputs
def read_all_lines(self, ):
with open(self.label_path, "r") as fin:
with open(self.label_path, "r", encoding='utf-8') as fin:
lines = fin.readlines()
return lines
......@@ -412,8 +434,8 @@ class XFUNDataset(Dataset):
return_data = {}
for k, v in data.items():
if k in self.return_keys:
if self.return_keys[k] == 'np':
v = np.array(v)
if self.return_keys[k]['type'] == 'np':
v = np.array(v, dtype=self.return_keys[k]['dtype'])
return_data[k] = v
return return_data
......
requirements.txt
浏览文件 @ 86b90aa9
......@@ -12,4 +12,5 @@ cython
lxml
premailer
openpyxl
fasttext==0.9.1
\ No newline at end of file
fasttext==0.9.1
test_tipc/configs/ch_PP-OCRv2_det/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -12,9 +12,9 @@ train_model_name:latest
train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
null:null
##
trainer:norm_train|pact_train
trainer:norm_train
norm_train:tools/train.py -c configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml -o
pact_train:deploy/slim/quantization/quant.py -c configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml -o
pact_train:null
fpgm_train:null
distill_train:null
null:null
......@@ -26,9 +26,9 @@ null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
Global.checkpoints:
norm_export:tools/export_model.py -c configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml -o
quant_export:deploy/slim/quantization/export_model.py -c configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml -o
quant_export:null
fpgm_export:
distill_export:null
export1:null
......
test_tipc/configs/ch_PP-OCRv2_det_PACT/train_infer_python.txt
浏览文件 @ 86b90aa9
===========================train_params===========================
model_name:PPOCRv2_det
model_name:ch_PPOCRv2_det_PACT
python:python3.7
gpu_list:0|0,1
Global.use_gpu:True|True
......
test_tipc/configs/ch_PP-OCRv2_rec/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -26,7 +26,7 @@ null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
Global.checkpoints:
norm_export:tools/export_model.py -c test_tipc/configs/ch_PP-OCRv2_rec/ch_PP-OCRv2_rec_distillation.yml -o
quant_export:
fpgm_export:
......@@ -43,7 +43,7 @@ inference:tools/infer/predict_rec.py
--cpu_threads:1|6
--rec_batch_num:1|6
--use_tensorrt:False|True
--precision:fp32|fp16|int8
--precision:fp32|int8
--rec_model_dir:
--image_dir:./inference/rec_inference
null:null
......
test_tipc/configs/ch_PP-OCRv2_rec_PACT/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -26,7 +26,7 @@ null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
Global.checkpoints:
norm_export:null
quant_export:deploy/slim/quantization/export_model.py -c test_tipc/configs/ch_PP-OCRv2_rec/ch_PP-OCRv2_rec_distillation.yml -o
fpgm_export: null
......@@ -34,7 +34,7 @@ distill_export:null
export1:null
export2:null
inference_dir:Student
infer_model:./inference/ch_PP-OCRv2_rec_infer
infer_model:./inference/ch_PP-OCRv2_rec_slim_quant_infer
infer_export:null
infer_quant:True
inference:tools/infer/predict_rec.py
......@@ -43,7 +43,7 @@ inference:tools/infer/predict_rec.py
--cpu_threads:1|6
--rec_batch_num:1|6
--use_tensorrt:False|True
--precision:fp32|fp16|int8
--precision:fp32|int8
--rec_model_dir:
--image_dir:./inference/rec_inference
null:null
......
test_tipc/configs/ch_ppocr_mobile_v2.0_det_PACT/train_infer_python.txt
浏览文件 @ 86b90aa9
===========================train_params===========================
model_name:ocr_det
model_name:ch_ppocr_mobile_v2.0_det_PACT
python:python3.7
gpu_list:0|0,1
Global.use_gpu:True|True
......@@ -34,7 +34,7 @@ distill_export:null
export1:null
export2:null
inference_dir:null
train_model:null
train_model:./inference/ch_ppocr_mobile_v2.0_det_prune_infer/
infer_export:null
infer_quant:False
inference:tools/infer/predict_det.py
......
test_tipc/configs/ch_ppocr_mobile_v2.0_rec/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -4,9 +4,9 @@ python:python3.7
gpu_list:0|0,1
Global.use_gpu:True|True
Global.auto_cast:null
Global.epoch_num:lite_train_infer=2|whole_train_infer=300
Global.epoch_num:lite_train_lite_infer=2|whole_train_whole_infer=300
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_infer=128|whole_train_infer=128
Train.loader.batch_size_per_card:lite_train_lite_infer=128|whole_train_whole_infer=128
Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./inference/rec_inference
......@@ -26,7 +26,7 @@ null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
Global.checkpoints:
norm_export:tools/export_model.py -c configs/rec/rec_icdar15_train.yml -o
quant_export:null
fpgm_export:null
......@@ -34,16 +34,16 @@ distill_export:null
export1:null
export2:null
##
infer_model:null
train_model:./inference/ch_ppocr_mobile_v2.0_rec_train/best_accuracy
infer_export:tools/export_model.py -c configs/rec/rec_icdar15_train.yml -o
infer_quant:False
inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100" --rec_algorithm="RARE"
inference:tools/infer/predict_rec.py
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1|6
--use_tensorrt:True|False
--precision:fp32|fp16|int8
--precision:fp32|int8
--rec_model_dir:
--image_dir:./inference/rec_inference
--save_log_path:./test/output/
......
test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -34,7 +34,7 @@ distill_export:null
export1:null
export2:null
inference_dir:null
train_model:null
infer_model:./inference/ch_ppocr_mobile_v2.0_rec_slim_infer/
infer_export:null
infer_quant:False
inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ppocr_keys_v1.txt --rec_image_shape="3,32,100"
......@@ -43,7 +43,7 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ppocr_ke
--cpu_threads:1|6
--rec_batch_num:1|6
--use_tensorrt:False|True
--precision:fp32|fp16|int8
--precision:fp32|int8
--rec_model_dir:
--image_dir:./inference/rec_inference
--save_log_path:./test/output/
......
test_tipc/configs/ch_ppocr_server_v2.0_rec/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -26,7 +26,7 @@ null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
Global.checkpoints:
norm_export:tools/export_model.py -c test_tipc/configs/ch_ppocr_server_v2.0_rec/rec_icdar15_train.yml -o
quant_export:null
fpgm_export:null
......@@ -34,7 +34,7 @@ distill_export:null
export1:null
export2:null
##
infer_model:null
train_model:./inference/ch_ppocr_server_v2.0_rec_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/ch_ppocr_server_v2.0_rec/rec_icdar15_train.yml -o
infer_quant:False
inference:tools/infer/predict_rec.py
......
test_tipc/configs/det_mv3_east_v2.0/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -34,15 +34,15 @@ distill_export:null
export1:null
export2:null
##
train_model:./inference/det_mv3_east/best_accuracy
infer_export:tools/export_model.py -c test_tipc/cconfigs/det_mv3_east_v2.0/det_mv3_east.yml -o
train_model:./inference/det_mv3_east_v2.0_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/det_mv3_east_v2.0/det_mv3_east.yml -o
infer_quant:False
inference:tools/infer/predict_det.py
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--use_tensorrt:False
--precision:fp32|fp16|int8
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
......
test_tipc/configs/det_mv3_pse_v2.0/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -34,16 +34,16 @@ distill_export:null
export1:null
export2:null
##
train_model:./inference/det_mv3_pse/best_accuracy
infer_export:tools/export_model.py -c test_tipc/cconfigs/det_mv3_pse_v2.0/det_mv3_pse.yml -o
train_model:./inference/det_mv3_pse_v2.0_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/det_mv3_pse_v2.0/det_mv3_pse.yml -o
infer_quant:False
inference:tools/infer/predict_det.py
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--precision:fp32|fp16|int8
--use_tensorrt:False
--precision:fp32|fp16
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
--save_log_path:null
......
test_tipc/configs/det_r50_vd_east_v2.0/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -34,15 +34,15 @@ distill_export:null
export1:null
export2:null
##
train_model:./inference/det_r50_vd_east/best_accuracy
infer_export:tools/export_model.py -c test_tipc/cconfigs/det_r50_vd_east_v2.0/det_r50_vd_east.yml -o
train_model:./inference/det_r50_vd_east_v2.0_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_east_v2.0/det_r50_vd_east.yml -o
infer_quant:False
inference:tools/infer/predict_det.py
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--use_tensorrt:False
--precision:fp32|fp16|int8
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
......
test_tipc/configs/det_r50_vd_pse_v2.0/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -34,7 +34,7 @@ distill_export:null
export1:null
export2:null
##
train_model:./inference/det_r50_vd_pse/best_accuracy
train_model:./inference/det_r50_vd_pse_v2.0_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_pse_v2.0/det_r50_vd_pse.yml -o
infer_quant:False
inference:tools/infer/predict_det.py
......@@ -42,7 +42,7 @@ inference:tools/infer/predict_det.py
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--use_tensorrt:False
--precision:fp32|fp16|int8
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
......
test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -42,7 +42,7 @@ inference:tools/infer/predict_det.py
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--use_tensorrt:False
--precision:fp32|fp16|int8
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
......
test_tipc/configs/det_r50_vd_sast_totaltext_v2.0/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -34,7 +34,7 @@ distill_export:null
export1:null
export2:null
inference_dir:null
train_model:./inference/det_r50_vd_sast_totaltext_v2.0/best_accuracy
train_model:./inference/det_r50_vd_sast_totaltext_v2.0_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_sast_totaltext_v2.0/det_r50_vd_sast_totaltext.yml -o
infer_quant:False
inference:tools/infer/predict_det.py
......@@ -42,7 +42,7 @@ inference:tools/infer/predict_det.py
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--use_tensorrt:False
--precision:fp32|fp16|int8
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
......
test_tipc/configs/rec_mtb_nrtr/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -26,7 +26,7 @@ null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
Global.checkpoints:
norm_export:tools/export_model.py -c test_tipc/configs/rec_mtb_nrtr/rec_mtb_nrtr.yml -o
quant_export:null
fpgm_export:null
......@@ -34,7 +34,7 @@ distill_export:null
export1:null
export2:null
##
infer_model:null
train_model:./inference/rec_mtb_nrtr_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/rec_mtb_nrtr/rec_mtb_nrtr.yml -o
infer_quant:False
inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/EN_symbol_dict.txt --rec_image_shape="1,32,100" --rec_algorithm="NRTR"
......
test_tipc/configs/rec_mv3_none_bilstm_ctc_v2.0/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -26,7 +26,7 @@ null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
Global.checkpoints:
norm_export:tools/export_model.py -c test_tipc/configs/rec_mv3_none_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
quant_export:null
fpgm_export:null
......@@ -34,7 +34,7 @@ distill_export:null
export1:null
export2:null
##
infer_model:null
train_model:./inference/rec_mv3_none_bilstm_ctc_v2.0_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/rec_mv3_none_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
infer_quant:False
inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100"
......
test_tipc/configs/rec_mv3_none_none_ctc_v2.0/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -26,7 +26,7 @@ null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
Global.checkpoints:
norm_export:tools/export_model.py -c test_tipc/configs/rec_mv3_none_none_ctc_v2.0/rec_icdar15_train.yml -o
quant_export:null
fpgm_export:null
......@@ -34,7 +34,7 @@ distill_export:null
export1:null
export2:null
##
infer_model:null
train_model:./inference/rec_mv3_none_none_ctc_v2.0_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/rec_mv3_none_none_ctc_v2.0/rec_icdar15_train.yml -o
infer_quant:False
inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100"
......
test_tipc/configs/rec_mv3_tps_bilstm_att_v2.0/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -26,7 +26,7 @@ null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
Global.checkpoints:
norm_export:tools/export_model.py -c test_tipc/configs/rec_mv3_tps_bilstm_att_v2.0/rec_mv3_tps_bilstm_att.yml -o
quant_export:null
fpgm_export:null
......@@ -34,7 +34,7 @@ distill_export:null
export1:null
export2:null
##
infer_model:null
train_model:./inference/rec_mv3_tps_bilstm_att_v2.0_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/rec_mv3_tps_bilstm_att_v2.0/rec_mv3_tps_bilstm_att.yml -o
infer_quant:False
inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100" --rec_algorithm="RARE"
......
test_tipc/configs/rec_mv3_tps_bilstm_ctc_v2.0/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -26,7 +26,7 @@ null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
Global.checkpoints:
norm_export:tools/export_model.py -c test_tipc/configs/rec_mv3_tps_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
quant_export:null
fpgm_export:null
......@@ -34,7 +34,7 @@ distill_export:null
export1:null
export2:null
##
infer_model:null
train_model:./inference/rec_mv3_tps_bilstm_ctc_v2.0_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/rec_mv3_tps_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
infer_quant:False
inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100" --rec_algorithm="StarNet"
......
test_tipc/configs/rec_r31_sar/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -26,7 +26,7 @@ null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
Global.checkpoints:
norm_export:tools/export_model.py -c test_tipc/configs/rec_r31_sar/rec_r31_sar.yml -o
quant_export:null
fpgm_export:null
......@@ -34,7 +34,7 @@ distill_export:null
export1:null
export2:null
##
infer_model:null
train_model:./inference/rec_r31_sar_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/rec_r31_sar/rec_r31_sar.yml -o
infer_quant:False
inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/dict90.txt --rec_image_shape="3,48,48,160" --rec_algorithm="SAR"
......@@ -43,7 +43,7 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/dict90.t
--cpu_threads:1|6
--rec_batch_num:1|6
--use_tensorrt:True|False
--precision:fp32|fp16|int8
--precision:fp32|int8
--rec_model_dir:
--image_dir:./inference/rec_inference
--save_log_path:./test/output/
......
test_tipc/configs/rec_r34_vd_none_bilstm_ctc_v2.0/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -26,7 +26,7 @@ null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
Global.checkpoints:
norm_export:tools/export_model.py -c test_tipc/configs/rec_r34_vd_none_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
quant_export:null
fpgm_export:null
......@@ -34,7 +34,7 @@ distill_export:null
export1:null
export2:null
##
infer_model:null
train_model:./inference/rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/rec_r34_vd_none_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
infer_quant:False
inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100"
......
test_tipc/configs/rec_r34_vd_none_none_ctc_v2.0/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -26,7 +26,7 @@ null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
Global.checkpoints:
norm_export:tools/export_model.py -c test_tipc/configs/rec_r34_vd_none_none_ctc_v2.0/rec_icdar15_train.yml -o
quant_export:null
fpgm_export:null
......@@ -34,7 +34,7 @@ distill_export:null
export1:null
export2:null
##
infer_model:null
train_model:./inference/rec_r34_vd_none_none_ctc_v2.0_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/rec_r34_vd_none_none_ctc_v2.0/rec_icdar15_train.yml -o
infer_quant:False
inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100"
......
test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2.0/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -26,7 +26,7 @@ null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
Global.checkpoints:
norm_export:tools/export_model.py -c test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2.0/rec_r34_vd_tps_bilstm_att.yml -o
quant_export:null
fpgm_export:null
......@@ -34,7 +34,7 @@ distill_export:null
export1:null
export2:null
##
infer_model:null
train_model:./inference/rec_r34_vd_tps_bilstm_att_v2.0_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2.0/rec_r34_vd_tps_bilstm_att.yml -o
infer_quant:False
inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100" --rec_algorithm="RARE"
......
test_tipc/configs/rec_r34_vd_tps_bilstm_ctc_v2.0/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -26,7 +26,7 @@ null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
Global.checkpoints:
norm_export:tools/export_model.py -c test_tipc/configs/rec_r34_vd_tps_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
quant_export:null
fpgm_export:null
......@@ -34,7 +34,7 @@ distill_export:null
export1:null
export2:null
##
infer_model:null
train_model:./inference/rec_r34_vd_tps_bilstm_ctc_v2.0_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/rec_r34_vd_tps_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
infer_quant:False
inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100" --rec_algorithm="StarNet"
......
test_tipc/configs/rec_r50_fpn_vd_none_srn/train_infer_python.txt
浏览文件 @ 86b90aa9
......@@ -26,7 +26,7 @@ null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
Global.checkpoints:
norm_export:tools/export_model.py -c test_tipc/configs/rec_r50_fpn_vd_none_srn/rec_r50_fpn_srn.yml -o
quant_export:null
fpgm_export:null
......@@ -34,7 +34,7 @@ distill_export:null
export1:null
export2:null
##
infer_model:null
train_model:./inference/rec_r50_vd_srn_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/rec_r50_fpn_vd_none_srn/rec_r50_fpn_srn.yml -o
infer_quant:False
inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="1,64,256" --rec_algorithm="SRN" --use_space_char=False
......
test_tipc/prepare.sh
浏览文件 @ 86b90aa9
......@@ -104,13 +104,17 @@ elif [ ${MODE} = "lite_train_whole_infer" ];then
elif [ ${MODE} = "whole_infer" ];then
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/rec_inference.tar --no-check-certificate
cd ./inference && tar xf rec_inference.tar && cd ../
cd ./inference && tar xf rec_inference.tar && tar xf ch_det_data_50.tar && cd ../
if [ ${model_name} = "ch_ppocr_mobile_v2.0_det" ]; then
eval_model_name="ch_ppocr_mobile_v2.0_det_train"
rm -rf ./train_data/icdar2015
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar --no-check-certificate
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar --no-check-certificate
cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && cd ../
elif [ ${model_name} = "ch_ppocr_mobile_v2.0_det_PACT" ]; then
eval_model_name="ch_ppocr_mobile_v2.0_det_prune_infer"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar --no-check-certificate
cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && cd ../
elif [ ${model_name} = "ch_ppocr_server_v2.0_det" ]; then
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar --no-check-certificate
cd ./inference && tar xf ch_ppocr_server_v2.0_det_train.tar && tar xf ch_det_data_50.tar && cd ../
......@@ -122,21 +126,13 @@ elif [ ${MODE} = "whole_infer" ];then
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar --no-check-certificate
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar --no-check-certificate
cd ./inference && tar xf ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_ppocr_server_v2.0_rec_infer.tar && tar xf ch_det_data_50.tar && cd ../
elif [ ${model_name} = "ch_ppocr_mobile_v2.0_rec" ]; then
eval_model_name="ch_ppocr_mobile_v2.0_rec_infer"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar --no-check-certificate
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
elif [ ${model_name} = "ch_ppocr_server_v2.0_rec" ]; then
eval_model_name="ch_ppocr_server_v2.0_rec_infer"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar --no-check-certificate
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
elif [ ${model_name} = "ch_ppocr_mobile_v2.0_rec_PACT" ]; then
eval_model_name="ch_PP-OCRv2_rec_slim_quant_train"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_train.tar --no-check-certificate
eval_model_name="ch_ppocr_mobile_v2.0_rec_slim_infer"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar --no-check-certificate
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
elif [ ${model_name} = "ch_ppocr_mobile_v2.0_rec_FPGM" ]; then
eval_model_name="ch_PP-OCRv2_rec_train"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar --no-check-certificate
eval_model_name="ch_PP-OCRv2_rec_infer"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar --no-check-certificate
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
fi
if [[ ${model_name} =~ "ch_PPOCRv2_det" ]]; then
......@@ -147,7 +143,8 @@ elif [ ${MODE} = "whole_infer" ];then
if [[ ${model_name} =~ "PPOCRv2_ocr_rec" ]]; then
eval_model_name="ch_PP-OCRv2_rec_infer"
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar --no-check-certificate
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar --no-check-certificate
cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_PP-OCRv2_rec_slim_quant_infer.tar && cd ../
fi
if [ ${model_name} == "en_server_pgnetA" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar --no-check-certificate
......@@ -157,6 +154,63 @@ elif [ ${MODE} = "whole_infer" ];then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar --no-check-certificate
cd ./inference/ && tar xf det_r50_vd_sast_icdar15_v2.0_train.tar && tar xf ch_det_data_50.tar && cd ../
fi
if [ ${model_name} == "rec_mv3_none_none_ctc_v2.0" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_none_ctc_v2.0_train.tar --no-check-certificate
cd ./inference/ && tar xf rec_mv3_none_none_ctc_v2.0_train.tar && cd ../
fi
if [ ${model_name} == "rec_r34_vd_none_none_ctc_v2.0" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_none_ctc_v2.0_train.tar --no-check-certificate
cd ./inference/ && tar xf rec_r34_vd_none_none_ctc_v2.0_train.tar && cd ../
fi
if [ ${model_name} == "rec_mv3_none_bilstm_ctc_v2.0" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar --no-check-certificate
cd ./inference/ && tar xf rec_mv3_none_bilstm_ctc_v2.0_train.tar && cd ../
fi
if [ ${model_name} == "rec_r34_vd_none_bilstm_ctc_v2.0" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar --no-check-certificate
cd ./inference/ && tar xf rec_r34_vd_none_bilstm_ctc_v2.0_train.tar && cd ../
fi
if [ ${model_name} == "rec_mv3_tps_bilstm_ctc_v2.0" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_ctc_v2.0_train.tar --no-check-certificate
cd ./inference/ && tar xf rec_mv3_tps_bilstm_ctc_v2.0_train.tar && cd ../
fi
if [ ${model_name} == "rec_r34_vd_tps_bilstm_ctc_v2.0" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar --no-check-certificate
cd ./inference/ && tar xf rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar && cd ../
fi
if [ ${model_name} == "ch_ppocr_server_v2.0_rec" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/ch_ppocr_server_v2.0_rec_train.tar --no-check-certificate
cd ./inference/ && tar xf ch_ppocr_server_v2.0_rec_train.tar && cd ../
fi
if [ ${model_name} == "ch_ppocr_mobile_v2.0_rec" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar --no-check-certificate
cd ./inference/ && tar xf ch_ppocr_mobile_v2.0_rec_train.tar && cd ../
fi
if [ ${model_name} == "rec_mtb_nrtr" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mtb_nrtr_train.tar --no-check-certificate
cd ./inference/ && tar xf rec_mtb_nrtr_train.tar && cd ../
fi
if [ ${model_name} == "rec_mv3_tps_bilstm_att_v2.0" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_att_v2.0_train.tar --no-check-certificate
cd ./inference/ && tar xf rec_mv3_tps_bilstm_att_v2.0_train.tar && cd ../
fi
if [ ${model_name} == "rec_r34_vd_tps_bilstm_att_v2.0" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_v2.0_train.tar --no-check-certificate
cd ./inference/ && tar xf rec_r34_vd_tps_bilstm_att_v2.0_train.tar && cd ../
fi
if [ ${model_name} == "rec_r31_sar" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar --no-check-certificate
cd ./inference/ && tar xf rec_r31_sar_train.tar && cd ../
fi
if [ ${model_name} == "rec_r50_fpn_vd_none_srn" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r50_vd_srn_train.tar --no-check-certificate
cd ./inference/ && tar xf rec_r50_vd_srn_train.tar && cd ../
fi
if [ ${model_name} == "det_r50_vd_sast_totaltext_v2.0" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar --no-check-certificate
cd ./inference/ && tar xf det_r50_vd_sast_totaltext_v2.0_train.tar && cd ../
fi
if [ ${model_name} == "det_mv3_db_v2.0" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar --no-check-certificate
cd ./inference/ && tar xf det_mv3_db_v2.0_train.tar && tar xf ch_det_data_50.tar && cd ../
......@@ -165,7 +219,24 @@ elif [ ${MODE} = "whole_infer" ];then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar --no-check-certificate
cd ./inference/ && tar xf det_r50_vd_db_v2.0_train.tar && tar xf ch_det_data_50.tar && cd ../
fi
if [ ${model_name} == "det_mv3_pse_v2.0" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar --no-check-certificate
cd ./inference/ && tar xf det_mv3_pse_v2.0_train.tar & cd ../
fi
if [ ${model_name} == "det_r50_vd_pse_v2.0" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar --no-check-certificate
cd ./inference/ && tar xf det_r50_vd_pse_v2.0_train.tar & cd ../
fi
if [ ${model_name} == "det_mv3_east_v2.0" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar --no-check-certificate
cd ./inference/ && tar xf det_mv3_east_v2.0_train.tar & cd ../
fi
if [ ${model_name} == "det_r50_vd_east_v2.0" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar --no-check-certificate
cd ./inference/ && tar xf det_r50_vd_east_v2.0_train.tar & cd ../
fi
fi
if [ ${MODE} = "klquant_whole_infer" ]; then
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar --no-check-certificate
cd ./train_data/ && tar xf icdar2015_lite.tar
......
tools/eval.py
浏览文件 @ 86b90aa9
......@@ -54,7 +54,8 @@ def main():
config['Architecture']["Head"]['out_channels'] = char_num
model = build_model(config['Architecture'])
extra_input = config['Architecture']['algorithm'] in ["SRN", "SAR"]
extra_input = config['Architecture'][
'algorithm'] in ["SRN", "NRTR", "SAR", "SEED"]
if "model_type" in config['Architecture'].keys():
model_type = config['Architecture']['model_type']
else:
......@@ -68,7 +69,6 @@ def main():
# build metric
eval_class = build_metric(config['Metric'])
# start eval
metric = program.eval(model, valid_dataloader, post_process_class,
eval_class, model_type, extra_input)
......
tools/infer/predict_cls.py
浏览文件 @ 86b90aa9
......@@ -145,8 +145,6 @@ def main(args):
for ino in range(len(img_list)):
logger.info("Predicts of {}:{}".format(valid_image_file_list[ino],
cls_res[ino]))
logger.info(
"The predict time about text angle classify module is as follows: ")
if __name__ == "__main__":
......
tools/infer_det.py
浏览文件 @ 86b90aa9
......@@ -126,9 +126,6 @@ def main():
otstr = file + "\t" + json.dumps(dt_boxes_json) + "\n"
fout.write(otstr.encode())
save_det_path = os.path.dirname(config['Global'][
'save_res_path']) + "/det_results/"
draw_det_res(boxes, config, src_img, file, save_det_path)
logger.info("success!")
......
tools/infer_kie.py 0 → 100755
浏览文件 @ 86b90aa9
# Copyright (c) 2020 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import paddle.nn.functional as F
import os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
import cv2
import paddle
from ppocr.data import create_operators, transform
from ppocr.modeling.architectures import build_model
from ppocr.utils.save_load import load_model
import tools.program as program
import time
def read_class_list(filepath):
dict = {}
with open(filepath, "r") as f:
lines = f.readlines()
for line in lines:
key, value = line.split(" ")
dict[key] = value.rstrip()
return dict
def draw_kie_result(batch, node, idx_to_cls, count):
img = batch[6].copy()
boxes = batch[7]
h, w = img.shape[:2]
pred_img = np.ones((h, w * 2, 3), dtype=np.uint8) * 255
max_value, max_idx = paddle.max(node, -1), paddle.argmax(node, -1)
node_pred_label = max_idx.numpy().tolist()
node_pred_score = max_value.numpy().tolist()
for i, box in enumerate(boxes):
if i >= len(node_pred_label):
break
new_box = [[box[0], box[1]], [box[2], box[1]], [box[2], box[3]],
[box[0], box[3]]]
Pts = np.array([new_box], np.int32)
cv2.polylines(
img, [Pts.reshape((-1, 1, 2))],
True,
color=(255, 255, 0),
thickness=1)
x_min = int(min([point[0] for point in new_box]))
y_min = int(min([point[1] for point in new_box]))
pred_label = str(node_pred_label[i])
if pred_label in idx_to_cls:
pred_label = idx_to_cls[pred_label]
pred_score = '{:.2f}'.format(node_pred_score[i])
text = pred_label + '(' + pred_score + ')'
cv2.putText(pred_img, text, (x_min * 2, y_min),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
vis_img = np.ones((h, w * 3, 3), dtype=np.uint8) * 255
vis_img[:, :w] = img
vis_img[:, w:] = pred_img
save_kie_path = os.path.dirname(config['Global'][
'save_res_path']) + "/kie_results/"
if not os.path.exists(save_kie_path):
os.makedirs(save_kie_path)
save_path = os.path.join(save_kie_path, str(count) + ".png")
cv2.imwrite(save_path, vis_img)
logger.info("The Kie Image saved in {}".format(save_path))
def main():
global_config = config['Global']
# build model
model = build_model(config['Architecture'])
load_model(config, model)
# create data ops
transforms = []
for op in config['Eval']['dataset']['transforms']:
transforms.append(op)
data_dir = config['Eval']['dataset']['data_dir']
ops = create_operators(transforms, global_config)
save_res_path = config['Global']['save_res_path']
class_path = config['Global']['class_path']
idx_to_cls = read_class_list(class_path)
if not os.path.exists(os.path.dirname(save_res_path)):
os.makedirs(os.path.dirname(save_res_path))
model.eval()
warmup_times = 0
count_t = []
with open(save_res_path, "wb") as fout:
with open(config['Global']['infer_img'], "rb") as f:
lines = f.readlines()
for index, data_line in enumerate(lines):
if index == 10:
warmup_t = time.time()
data_line = data_line.decode('utf-8')
substr = data_line.strip("\n").split("\t")
img_path, label = data_dir + "/" + substr[0], substr[1]
data = {'img_path': img_path, 'label': label}
with open(data['img_path'], 'rb') as f:
img = f.read()
data['image'] = img
st = time.time()
batch = transform(data, ops)
batch_pred = [0] * len(batch)
for i in range(len(batch)):
batch_pred[i] = paddle.to_tensor(
np.expand_dims(
batch[i], axis=0))
st = time.time()
node, edge = model(batch_pred)
node = F.softmax(node, -1)
count_t.append(time.time() - st)
draw_kie_result(batch, node, idx_to_cls, index)
logger.info("success!")
logger.info("It took {} s for predict {} images.".format(
np.sum(count_t), len(count_t)))
ips = len(count_t[warmup_times:]) / np.sum(count_t[warmup_times:])
logger.info("The ips is {} images/s".format(ips))
if __name__ == '__main__':
config, device, logger, vdl_writer = program.preprocess()
main()
tools/program.py
浏览文件 @ 86b90aa9
......@@ -239,6 +239,8 @@ def train(config,
else:
if model_type == 'table' or extra_input:
preds = model(images, data=batch[1:])
elif model_type == "kie":
preds = model(batch)
else:
preds = model(images)
loss = loss_class(preds, batch)
......@@ -266,7 +268,7 @@ def train(config,
if cal_metric_during_train: # only rec and cls need
batch = [item.numpy() for item in batch]
if model_type == 'table':
if model_type in ['table', 'kie']:
eval_class(preds, batch)
else:
post_result = post_process_class(preds, batch[1])
......@@ -399,17 +401,20 @@ def eval(model,
start = time.time()
if model_type == 'table' or extra_input:
preds = model(images, data=batch[1:])
elif model_type == "kie":
preds = model(batch)
else:
preds = model(images)
batch = [item.numpy() for item in batch]
# Obtain usable results from post-processing methods
total_time += time.time() - start
# Evaluate the results of the current batch
if model_type == 'table':
if model_type in ['table', 'kie']:
eval_class(preds, batch)
else:
post_result = post_process_class(preds, batch[1])
eval_class(post_result, batch)
pbar.update(1)
total_frame += len(images)
# Get final metric，eg. acc or hmean
......@@ -498,8 +503,13 @@ def preprocess(is_train=False):
assert alg in [
'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE',
'SEED'
'SEED', 'SDMGR'
]
windows_not_support_list = ['PSE']
if platform.system() == "Windows" and alg in windows_not_support_list:
logger.warning('{} is not support in Windows now'.format(
windows_not_support_list))
sys.exit()
device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'
device = paddle.set_device(device)
......
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