Skip to content

P
PaddleOCR

PaddlePaddle / PaddleOCR
大约 2 年 前同步成功

通知 1558
Star 32965
Fork 6643
P
PaddleOCR
提交 6048ea5c 编写于 作者: W WenmuZhou
浏览文件
操作

merge release/2.2

上级 b349e854 a57e1186
展开全部 隐藏空白更改
内联 并排
Showing with 534 addition and 1042 deletion
README.md
浏览文件 @ 6048ea5c
English | [简体中文](README_ch.md) English | [简体中文](README_ch.md)
------------------------------------------------------------------------------------------
<p align="left">
<a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache%202-dfd.svg"></a>
<a href="https://github.com/PaddlePaddle/PaddleOCR/releases"><img src="https://img.shields.io/github/v/release/PaddlePaddle/PaddleOCR?color=ffa"></a>
<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>
## Introduction ## Introduction
PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools that help users train better models and apply them into practice. PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools that help users train better models and apply them into practice.
...@@ -9,6 +22,7 @@ PaddleOCR supports both dynamic graph and static graph programming paradigm ...@@ -9,6 +22,7 @@ PaddleOCR supports both dynamic graph and static graph programming paradigm
- Static graph: develop branch - Static graph: develop branch
**Recent updates** **Recent updates**
- 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.
- 2021.1.21 update more than 25+ multilingual recognition models [models list](./doc/doc_en/models_list_en.md), including：English, Chinese, German, French, Japanese，Spanish，Portuguese Russia Arabic and so on. Models for more languages will continue to be updated [Develop Plan](https://github.com/PaddlePaddle/PaddleOCR/issues/1048). - 2021.1.21 update more than 25+ multilingual recognition models [models list](./doc/doc_en/models_list_en.md), including：English, Chinese, German, French, Japanese，Spanish，Portuguese Russia Arabic and so on. Models for more languages will continue to be updated [Develop Plan](https://github.com/PaddlePaddle/PaddleOCR/issues/1048).
- 2020.12.15 update Data synthesis tool, i.e., [Style-Text](./StyleText/README.md)，easy to synthesize a large number of images which are similar to the target scene image. - 2020.12.15 update Data synthesis tool, i.e., [Style-Text](./StyleText/README.md)，easy to synthesize a large number of images which are similar to the target scene image.
- 2020.11.25 Update a new data annotation tool, i.e., [PPOCRLabel](./PPOCRLabel/README.md), which is helpful to improve the labeling efficiency. Moreover, the labeling results can be used in training of the PP-OCR system directly. - 2020.11.25 Update a new data annotation tool, i.e., [PPOCRLabel](./PPOCRLabel/README.md), which is helpful to improve the labeling efficiency. Moreover, the labeling results can be used in training of the PP-OCR system directly.
...@@ -79,7 +93,8 @@ For a new language request, please refer to [Guideline for new language_requests ...@@ -79,7 +93,8 @@ For a new language request, please refer to [Guideline for new language_requests
## Tutorials ## Tutorials
- [Installation](./doc/doc_en/installation_en.md) - [Installation](./doc/doc_en/installation_en.md)
- [Quick Start](./doc/doc_en/quickstart_en.md) - [Quick Start(Chinese)](./doc/doc_en/quickstart_en.md)
- [Quick Start(English&Multi-languages)](./doc/doc_en/multi_languages_en.md)
- [Code Structure](./doc/doc_en/tree_en.md) - [Code Structure](./doc/doc_en/tree_en.md)
- Algorithm Introduction - Algorithm Introduction
- [Text Detection Algorithm](./doc/doc_en/algorithm_overview_en.md) - [Text Detection Algorithm](./doc/doc_en/algorithm_overview_en.md)
......
README_ch.md
浏览文件 @ 6048ea5c
[English](README.md) | 简体中文 [English](README.md) | 简体中文
------------------------------------------------------------------------------------------
<p align="left">
<a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache%202-dfd.svg"></a>
<a href="https://github.com/PaddlePaddle/PaddleOCR/releases"><img src="https://img.shields.io/github/v/release/PaddlePaddle/PaddleOCR?color=ffa"></a>
<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>
## 简介 ## 简介
PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。 PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。
## 注意 ## 注意
PaddleOCR同时支持动态图与静态图两种编程范式 PaddleOCR同时支持动态图与静态图两种编程范式
- 动态图版本：dygraph分支（默认），需将paddle版本升级至2.0.0（[快速安装](./doc/doc_ch/installation.md)） - 动态图版本：release/2.2（默认分支，开发分支为dygraph分支），需将paddle版本升级至2.0.0或以上版本（[快速安装](./doc/doc_ch/installation.md)）
- 静态图版本：develop分支 - 静态图版本：develop分支
**近期更新** **近期更新**
- PaddleOCR研发团队对最新发版内容技术深入解读，8月4日晚上20:30，[直播地址](https://live.bilibili.com/21689802)。
- 2021.8.3 正式发布PaddleOCR v2.2，新增文档结构分析[PP-Structure](./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](./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。
- 2021.4.8 release 2.1版本，新增AAAI 2021论文[端到端识别算法PGNet](./doc/doc_ch/pgnet.md)开源，[多语言模型](./doc/doc_ch/multi_languages.md)支持种类增加到80+。 - 2021.4.8 release 2.1版本，新增AAAI 2021论文[端到端识别算法PGNet](./doc/doc_ch/pgnet.md)开源，[多语言模型](./doc/doc_ch/multi_languages.md)支持种类增加到80+。
- 2021.2.1 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，总数162个，每周一都会更新，欢迎大家持续关注。 - 2021.2.8 正式发布PaddleOCRv2.0(branch release/2.0)并设置为推荐用户使用的默认分支. 发布的详细内容，请参考: https://github.com/PaddlePaddle/PaddleOCR/releases/tag/v2.0.0
- 2021.1.21 更新多语言识别模型，目前支持语种超过27种，包括中文简体、中文繁体、英文、法文、德文、韩文、日文、意大利文、西班牙文、葡萄牙文、俄罗斯文、阿拉伯文等，后续计划可以参考[多语言研发计划](https://github.com/PaddlePaddle/PaddleOCR/issues/1048) - 2021.1.26,28,29 PaddleOCR官方研发团队带来技术深入解读三日直播课，1月26日、28日、29日晚上19:30，[直播地址](https://live.bilibili.com/21689802)
- 2020.12.15 更新数据合成工具[Style-Text](./StyleText/README_ch.md)，可以批量合成大量与目标场景类似的图像，在多个场景验证，效果明显提升。
- 2020.11.25 更新半自动标注工具[PPOCRLabel](./PPOCRLabel/README_ch.md)，辅助开发者高效完成标注任务，输出格式与PP-OCR训练任务完美衔接。
- 2020.9.22 更新PP-OCR技术文章，https://arxiv.org/abs/2009.09941
- [More](./doc/doc_ch/update.md) - [More](./doc/doc_ch/update.md)
...@@ -24,7 +37,7 @@ PaddleOCR同时支持动态图与静态图两种编程范式 ...@@ -24,7 +37,7 @@ PaddleOCR同时支持动态图与静态图两种编程范式
- 超轻量ppocr_mobile移动端系列：检测（3.0M）+方向分类器（1.4M）+ 识别（5.0M）= 9.4M - 超轻量ppocr_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 - 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 - 支持80+多语言识别，详见[多语言模型](./doc/doc_ch/multi_languages.md)
- 丰富易用的OCR相关工具组件 - 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 - 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 - 数据合成工具Style-Text：批量合成大量与目标场景类似的图像
...@@ -90,7 +103,7 @@ PaddleOCR同时支持动态图与静态图两种编程范式 ...@@ -90,7 +103,7 @@ PaddleOCR同时支持动态图与静态图两种编程范式
- [基于pip安装whl包快速推理](./doc/doc_ch/whl.md) - [基于pip安装whl包快速推理](./doc/doc_ch/whl.md)
- [基于Python脚本预测引擎推理](./doc/doc_ch/inference.md) - [基于Python脚本预测引擎推理](./doc/doc_ch/inference.md)
- [基于C++预测引擎推理](./deploy/cpp_infer/readme.md) - [基于C++预测引擎推理](./deploy/cpp_infer/readme.md)
- [服务化部署](./deploy/pdserving/README_CN.md) - [服务化部署](./deploy/hubserving/readme.md)
- [端侧部署](./deploy/lite/readme.md) - [端侧部署](./deploy/lite/readme.md)
- [Benchmark](./doc/doc_ch/benchmark.md) - [Benchmark](./doc/doc_ch/benchmark.md)
- 数据集 - 数据集
...@@ -105,8 +118,8 @@ PaddleOCR同时支持动态图与静态图两种编程范式 ...@@ -105,8 +118,8 @@ PaddleOCR同时支持动态图与静态图两种编程范式
- [效果展示](#效果展示) - [效果展示](#效果展示)
- FAQ - FAQ
- [【精选】OCR精选10个问题](./doc/doc_ch/FAQ.md) - [【精选】OCR精选10个问题](./doc/doc_ch/FAQ.md)
- [【理论篇】OCR通用32个问题](./doc/doc_ch/FAQ.md) - [【理论篇】OCR通用50个问题](./doc/doc_ch/FAQ.md)
- [【实战篇】PaddleOCR实战110个问题](./doc/doc_ch/FAQ.md) - [【实战篇】PaddleOCR实战183个问题](./doc/doc_ch/FAQ.md)
- [技术交流群](#欢迎加入PaddleOCR技术交流群) - [技术交流群](#欢迎加入PaddleOCR技术交流群)
- [参考文献](./doc/doc_ch/reference.md) - [参考文献](./doc/doc_ch/reference.md)
- [许可证书](#许可证书) - [许可证书](#许可证书)
......
configs/det/ch_ppocr_v2.1/ch_det_lite_train_cml_v2.1.yml 已删除 100644 → 0
浏览文件 @ b349e854
Global:
use_gpu: true
epoch_num: 1200
log_smooth_window: 20
print_batch_step: 2
save_model_dir: ./output/ch_db_mv3/
save_epoch_step: 1200
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [3000, 2000]
cal_metric_during_train: False
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_en/img_10.jpg
save_res_path: ./output/det_db/predicts_db.txt
Architecture:
name: DistillationModel
algorithm: Distillation
Models:
Student:
pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
freeze_params: false
return_all_feats: false
model_type: det
algorithm: DB
Backbone:
name: MobileNetV3
scale: 0.5
model_name: large
disable_se: True
Neck:
name: DBFPN
out_channels: 96
Head:
name: DBHead
k: 50
Student2:
pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
freeze_params: false
return_all_feats: false
model_type: det
algorithm: DB
Transform:
Backbone:
name: MobileNetV3
scale: 0.5
model_name: large
disable_se: True
Neck:
name: DBFPN
out_channels: 96
Head:
name: DBHead
k: 50
Teacher:
pretrained: ./pretrain_models/ch_ppocr_server_v2.0_det_train/best_accuracy
freeze_params: true
return_all_feats: false
model_type: det
algorithm: DB
Transform:
Backbone:
name: ResNet
layers: 18
Neck:
name: DBFPN
out_channels: 256
Head:
name: DBHead
k: 50
Loss:
name: CombinedLoss
loss_config_list:
- DistillationDilaDBLoss:
weight: 1.0
model_name_pairs:
- ["Student", "Teacher"]
- ["Student2", "Teacher"]
key: maps
balance_loss: true
main_loss_type: DiceLoss
alpha: 5
beta: 10
ohem_ratio: 3
- DistillationDMLLoss:
model_name_pairs:
- ["Student", "Student2"]
maps_name: "thrink_maps"
weight: 1.0
# act: None
model_name_pairs: ["Student", "Student2"]
key: maps
- DistillationDBLoss:
weight: 1.0
model_name_list: ["Student", "Student2"]
# key: maps
# name: DBLoss
balance_loss: true
main_loss_type: DiceLoss
alpha: 5
beta: 10
ohem_ratio: 3
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: Cosine
learning_rate: 0.001
warmup_epoch: 2
regularizer:
name: 'L2'
factor: 0
PostProcess:
name: DistillationDBPostProcess
model_name: ["Student", "Student2", "Teacher"]
# key: maps
thresh: 0.3
box_thresh: 0.6
max_candidates: 1000
unclip_ratio: 1.5
Metric:
name: DistillationMetric
base_metric_name: DetMetric
main_indicator: hmean
key: "Student"
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/icdar2015/text_localization/
label_file_list:
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
ratio_list: [1.0]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- IaaAugment:
augmenter_args:
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
- { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
- { 'type': Resize, 'args': { 'size': [0.5, 3] } }
- EastRandomCropData:
size: [960, 960]
max_tries: 50
keep_ratio: true
- MakeBorderMap:
shrink_ratio: 0.4
thresh_min: 0.3
thresh_max: 0.7
- MakeShrinkMap:
shrink_ratio: 0.4
min_text_size: 8
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
loader:
shuffle: True
drop_last: False
batch_size_per_card: 8
num_workers: 4
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/icdar2015/text_localization/
label_file_list:
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- DetResizeForTest:
# image_shape: [736, 1280]
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 1 # must be 1
num_workers: 2
configs/det/ch_ppocr_v2.1/ch_det_lite_train_distill_v2.1.yml 已删除 100644 → 0
浏览文件 @ b349e854
Global:
use_gpu: true
epoch_num: 1200
log_smooth_window: 20
print_batch_step: 2
save_model_dir: ./output/ch_db_mv3/
save_epoch_step: 1200
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [3000, 2000]
cal_metric_during_train: False
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_en/img_10.jpg
save_res_path: ./output/det_db/predicts_db.txt
Architecture:
name: DistillationModel
algorithm: Distillation
Models:
Student:
pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
freeze_params: false
return_all_feats: false
model_type: det
algorithm: DB
Backbone:
name: MobileNetV3
scale: 0.5
model_name: large
disable_se: True
Neck:
name: DBFPN
out_channels: 96
Head:
name: DBHead
k: 50
Teacher:
pretrained: ./pretrain_models/ch_ppocr_server_v2.0_det_train/best_accuracy
freeze_params: true
return_all_feats: false
model_type: det
algorithm: DB
Transform:
Backbone:
name: ResNet
layers: 18
Neck:
name: DBFPN
out_channels: 256
Head:
name: DBHead
k: 50
Loss:
name: CombinedLoss
loss_config_list:
- DistillationDilaDBLoss:
weight: 1.0
model_name_pairs:
- ["Student", "Teacher"]
key: maps
balance_loss: true
main_loss_type: DiceLoss
alpha: 5
beta: 10
ohem_ratio: 3
- DistillationDBLoss:
weight: 1.0
model_name_list: ["Student", "Teacher"]
# key: maps
name: DBLoss
balance_loss: true
main_loss_type: DiceLoss
alpha: 5
beta: 10
ohem_ratio: 3
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: Cosine
learning_rate: 0.001
warmup_epoch: 2
regularizer:
name: 'L2'
factor: 0
PostProcess:
name: DistillationDBPostProcess
model_name: ["Student", "Student2"]
key: head_out
thresh: 0.3
box_thresh: 0.6
max_candidates: 1000
unclip_ratio: 1.5
Metric:
name: DistillationMetric
base_metric_name: DetMetric
main_indicator: hmean
key: "Student"
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/icdar2015/text_localization/
label_file_list:
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
ratio_list: [1.0]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- IaaAugment:
augmenter_args:
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
- { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
- { 'type': Resize, 'args': { 'size': [0.5, 3] } }
- EastRandomCropData:
size: [960, 960]
max_tries: 50
keep_ratio: true
- MakeBorderMap:
shrink_ratio: 0.4
thresh_min: 0.3
thresh_max: 0.7
- MakeShrinkMap:
shrink_ratio: 0.4
min_text_size: 8
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
loader:
shuffle: True
drop_last: False
batch_size_per_card: 8
num_workers: 4
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/icdar2015/text_localization/
label_file_list:
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- DetResizeForTest:
# image_shape: [736, 1280]
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 1 # must be 1
num_workers: 2
configs/det/ch_ppocr_v2.1/ch_det_lite_train_dml_v2.1.yml 已删除 100644 → 0
浏览文件 @ b349e854
Global:
use_gpu: true
epoch_num: 1200
log_smooth_window: 20
print_batch_step: 2
save_model_dir: ./output/ch_db_mv3/
save_epoch_step: 1200
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [3000, 2000]
cal_metric_during_train: False
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_en/img_10.jpg
save_res_path: ./output/det_db/predicts_db.txt
Architecture:
name: DistillationModel
algorithm: Distillation
Models:
Student:
pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
freeze_params: false
return_all_feats: false
model_type: det
algorithm: DB
Backbone:
name: MobileNetV3
scale: 0.5
model_name: large
disable_se: True
Neck:
name: DBFPN
out_channels: 96
Head:
name: DBHead
k: 50
Student2:
pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
freeze_params: false
return_all_feats: false
model_type: det
algorithm: DB
Transform:
Backbone:
name: MobileNetV3
scale: 0.5
model_name: large
disable_se: True
Neck:
name: DBFPN
out_channels: 96
Head:
name: DBHead
k: 50
Loss:
name: CombinedLoss
loss_config_list:
- DistillationDMLLoss:
model_name_pairs:
- ["Student", "Student2"]
maps_name: "thrink_maps"
weight: 1.0
act: "softmax"
model_name_pairs: ["Student", "Student2"]
key: maps
- DistillationDBLoss:
weight: 1.0
model_name_list: ["Student", "Student2"]
# key: maps
name: DBLoss
balance_loss: true
main_loss_type: DiceLoss
alpha: 5
beta: 10
ohem_ratio: 3
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: Cosine
learning_rate: 0.001
warmup_epoch: 2
regularizer:
name: 'L2'
factor: 0
PostProcess:
name: DistillationDBPostProcess
model_name: ["Student", "Student2"]
key: head_out
thresh: 0.3
box_thresh: 0.6
max_candidates: 1000
unclip_ratio: 1.5
Metric:
name: DistillationMetric
base_metric_name: DetMetric
main_indicator: hmean
key: "Student"
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/icdar2015/text_localization/
label_file_list:
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
ratio_list: [1.0]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- IaaAugment:
augmenter_args:
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
- { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
- { 'type': Resize, 'args': { 'size': [0.5, 3] } }
- EastRandomCropData:
size: [960, 960]
max_tries: 50
keep_ratio: true
- MakeBorderMap:
shrink_ratio: 0.4
thresh_min: 0.3
thresh_max: 0.7
- MakeShrinkMap:
shrink_ratio: 0.4
min_text_size: 8
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
loader:
shuffle: True
drop_last: False
batch_size_per_card: 8
num_workers: 4
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/icdar2015/text_localization/
label_file_list:
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- DetResizeForTest:
# image_shape: [736, 1280]
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 1 # must be 1
num_workers: 2
configs/rec/ch_ppocr_v2.1/rec_chinese_lite_train_distillation_v2.1.yml 已删除 100644 → 0
浏览文件 @ b349e854
Global:
debug: false
use_gpu: true
epoch_num: 800
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec_chinese_lite_distillation_v2.1
save_epoch_step: 3
eval_batch_step: [0, 2000]
cal_metric_during_train: true
pretrained_model:
checkpoints:
save_inference_dir:
use_visualdl: false
infer_img: doc/imgs_words/ch/word_1.jpg
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
character_type: ch
max_text_length: 25
infer_mode: false
use_space_char: true
distributed: true
save_res_path: ./output/rec/predicts_chinese_lite_distillation_v2.1.txt
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: Piecewise
decay_epochs : [700, 800]
values : [0.001, 0.0001]
warmup_epoch: 5
regularizer:
name: L2
factor: 2.0e-05
Architecture:
model_type: &model_type "rec"
name: DistillationModel
algorithm: Distillation
Models:
Teacher:
pretrained:
freeze_params: false
return_all_feats: true
model_type: *model_type
algorithm: CRNN
Transform:
Backbone:
name: MobileNetV1Enhance
scale: 0.5
Neck:
name: SequenceEncoder
encoder_type: rnn
hidden_size: 64
Head:
name: CTCHead
mid_channels: 96
fc_decay: 0.00002
Student:
pretrained:
freeze_params: false
return_all_feats: true
model_type: *model_type
algorithm: CRNN
Transform:
Backbone:
name: MobileNetV1Enhance
scale: 0.5
Neck:
name: SequenceEncoder
encoder_type: rnn
hidden_size: 64
Head:
name: CTCHead
mid_channels: 96
fc_decay: 0.00002
Loss:
name: CombinedLoss
loss_config_list:
- DistillationCTCLoss:
weight: 1.0
model_name_list: ["Student", "Teacher"]
key: head_out
- DistillationDMLLoss:
weight: 1.0
act: "softmax"
model_name_pairs:
- ["Student", "Teacher"]
key: head_out
- DistillationDistanceLoss:
weight: 1.0
mode: "l2"
model_name_pairs:
- ["Student", "Teacher"]
key: backbone_out
PostProcess:
name: DistillationCTCLabelDecode
model_name: ["Student", "Teacher"]
key: head_out
Metric:
name: DistillationMetric
base_metric_name: RecMetric
main_indicator: acc
key: "Student"
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/
label_file_list:
- ./train_data/train_list.txt
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- RecAug:
- CTCLabelEncode:
- RecResizeImg:
image_shape: [3, 32, 320]
- KeepKeys:
keep_keys:
- image
- label
- length
loader:
shuffle: true
batch_size_per_card: 128
drop_last: true
num_sections: 1
num_workers: 8
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data
label_file_list:
- ./train_data/val_list.txt
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- CTCLabelEncode:
- RecResizeImg:
image_shape: [3, 32, 320]
- KeepKeys:
keep_keys:
- image
- label
- length
loader:
shuffle: false
drop_last: false
batch_size_per_card: 128
num_workers: 8
doc/doc_ch/FAQ.md
浏览文件 @ 6048ea5c
此差异已折叠。
doc/doc_ch/knowledge_distillation.md 已删除 100644 → 0
浏览文件 @ b349e854
# 知识蒸馏
## 1. 简介
### 1.1 知识蒸馏介绍
近年来，深度神经网络在计算机视觉、自然语言处理等领域被验证是一种极其有效的解决问题的方法。通过构建合适的神经网络，加以训练，最终网络模型的性能指标基本上都会超过传统算法。
在数据量足够大的情况下，通过合理构建网络模型的方式增加其参数量，可以显著改善模型性能，但是这又带来了模型复杂度急剧提升的问题。大模型在实际场景中使用的成本较高。
深度神经网络一般有较多的参数冗余，目前有几种主要的方法对模型进行压缩，减小其参数量。如裁剪、量化、知识蒸馏等，其中知识蒸馏是指使用教师模型(teacher model)去指导学生模型(student model)学习特定任务，保证小模型在参数量不变的情况下，得到比较大的性能提升。
此外，在知识蒸馏任务中，也衍生出了互学习的模型训练方法，论文[Deep Mutual Learning](https://arxiv.org/abs/1706.00384)中指出，使用两个完全相同的模型在训练的过程中互相监督，可以达到比单个模型训练更好的效果。
### 1.2 PaddleOCR知识蒸馏简介
无论是大模型蒸馏小模型，还是小模型之间互相学习，更新参数，他们本质上是都是不同模型之间输出或者特征图(feature map)之间的相互监督，区别仅在于 (1) 模型是否需要固定参数。(2) 模型是否需要加载预训练模型。
对于大模型蒸馏小模型的情况，大模型一般需要加载预训练模型并固定参数；对于小模型之间互相蒸馏的情况，小模型一般都不加载预训练模型，参数也都是可学习的状态。
在知识蒸馏任务中，不只有2个模型之间进行蒸馏的情况，多个模型之间互相学习的情况也非常普遍。因此在知识蒸馏代码框架中，也有必要支持该种类别的蒸馏方法。
PaddleOCR中集成了知识蒸馏的算法，具体地，有以下几个主要的特点：
- 支持任意网络的互相学习，不要求子网络结构完全一致或者具有预训练模型；同时子网络数量也没有任何限制，只需要在配置文件中添加即可。
- 支持loss函数通过配置文件任意配置，不仅可以使用某种loss，也可以使用多种loss的组合
- 支持知识蒸馏训练、预测、评估与导出等所有模型相关的环境，方便使用与部署。
通过知识蒸馏，在中英文通用文字识别任务中，不增加任何预测耗时的情况下，可以给模型带来3%以上的精度提升，结合学习率调整策略以及模型结构微调策略，最终提升提升超过5%。
## 2. 配置文件解析
在知识蒸馏训练的过程中，数据预处理、优化器、学习率、全局的一些属性没有任何变化。模型结构、损失函数、后处理、指标计算等模块的配置文件需要进行微调。
下面以识别与检测的知识蒸馏配置文件为例，对知识蒸馏的训练与配置进行解析。
### 2.1 识别配置文件解析
配置文件在[rec_chinese_lite_train_distillation_v2.1.yml](../../configs/rec/ch_ppocr_v2.1/rec_chinese_lite_train_distillation_v2.1.yml)。
#### 2.1.1 模型结构
知识蒸馏任务中，模型结构配置如下所示。
```yaml
Architecture:
model_type: &model_type "rec" # 模型类别，rec、det等，每个子网络的的模型类别都与
name: DistillationModel # 结构名称，蒸馏任务中，为DistillationModel，用于构建对应的结构
algorithm: Distillation # 算法名称
Models: # 模型，包含子网络的配置信息
Teacher: # 子网络名称，至少需要包含`pretrained`与`freeze_params`信息，其他的参数为子网络的构造参数
pretrained: # 该子网络是否需要加载预训练模型
freeze_params: false # 是否需要固定参数
return_all_feats: true # 子网络的参数，表示是否需要返回所有的features，如果为False，则只返回最后的输出
model_type: *model_type # 模型类别
algorithm: CRNN # 子网络的算法名称，该子网络剩余参与均为构造参数，与普通的模型训练配置一致
Transform:
Backbone:
name: MobileNetV1Enhance
scale: 0.5
Neck:
name: SequenceEncoder
encoder_type: rnn
hidden_size: 64
Head:
name: CTCHead
mid_channels: 96
fc_decay: 0.00002
Student: # 另外一个子网络，这里给的是DML的蒸馏示例，两个子网络结构相同，均需要学习参数
pretrained: # 下面的组网参数同上
freeze_params: false
return_all_feats: true
model_type: *model_type
algorithm: CRNN
Transform:
Backbone:
name: MobileNetV1Enhance
scale: 0.5
Neck:
name: SequenceEncoder
encoder_type: rnn
hidden_size: 64
Head:
name: CTCHead
mid_channels: 96
fc_decay: 0.00002
```
当然，这里如果希望添加更多的子网络进行训练，也可以按照`Student`与`Teacher`的添加方式，在配置文件中添加相应的字段。比如说如果希望有3个模型互相监督，共同训练，那么`Architecture`可以写为如下格式。
```yaml
Architecture:
model_type: &model_type "rec"
name: DistillationModel
algorithm: Distillation
Models:
Teacher:
pretrained:
freeze_params: false
return_all_feats: true
model_type: *model_type
algorithm: CRNN
Transform:
Backbone:
name: MobileNetV1Enhance
scale: 0.5
Neck:
name: SequenceEncoder
encoder_type: rnn
hidden_size: 64
Head:
name: CTCHead
mid_channels: 96
fc_decay: 0.00002
Student:
pretrained:
freeze_params: false
return_all_feats: true
model_type: *model_type
algorithm: CRNN
Transform:
Backbone:
name: MobileNetV1Enhance
scale: 0.5
Neck:
name: SequenceEncoder
encoder_type: rnn
hidden_size: 64
Head:
name: CTCHead
mid_channels: 96
fc_decay: 0.00002
Student2: # 知识蒸馏任务中引入的新的子网络，其他部分与上述配置相同
pretrained:
freeze_params: false
return_all_feats: true
model_type: *model_type
algorithm: CRNN
Transform:
Backbone:
name: MobileNetV1Enhance
scale: 0.5
Neck:
name: SequenceEncoder
encoder_type: rnn
hidden_size: 64
Head:
name: CTCHead
mid_channels: 96
fc_decay: 0.00002
```
最终该模型训练时，包含3个子网络：`Teacher`, `Student`, `Student2`。
蒸馏模型`DistillationModel`类的具体实现代码可以参考[distillation_model.py](../../ppocr/modeling/architectures/distillation_model.py)。
最终模型`forward`输出为一个字典，key为所有的子网络名称，例如这里为`Student`与`Teacher`，value为对应子网络的输出，可以为`Tensor`（只返回该网络的最后一层）和`dict`（也返回了中间的特征信息）。
在识别任务中，为了添加更多损失函数，保证蒸馏方法的可扩展性，将每个子网络的输出保存为`dict`，其中包含子模块输出。以该识别模型为例，每个子网络的输出结果均为`dict`，key包含`backbone_out`,`neck_out`, `head_out`，`value`为对应模块的tensor，最终对于上述配置文件，`DistillationModel`的输出格式如下。
```json
{
"Teacher": {
"backbone_out": tensor,
"neck_out": tensor,
"head_out": tensor,
},
"Student": {
"backbone_out": tensor,
"neck_out": tensor,
"head_out": tensor,
}
}
```
#### 2.1.2 损失函数
知识蒸馏任务中，损失函数配置如下所示。
```yaml
Loss:
name: CombinedLoss # 损失函数名称，基于改名称，构建用于损失函数的类
loss_config_list: # 损失函数配置文件列表，为CombinedLoss的必备函数
- DistillationCTCLoss: # 基于蒸馏的CTC损失函数，继承自标准的CTC loss
weight: 1.0 # 损失函数的权重，loss_config_list中，每个损失函数的配置都必须包含该字段
model_name_list: ["Student", "Teacher"] # 对于蒸馏模型的预测结果，提取这两个子网络的输出，与gt计算CTC loss
key: head_out # 取子网络输出dict中，该key对应的tensor
- DistillationDMLLoss: # 蒸馏的DML损失函数，继承自标准的DMLLoss
weight: 1.0 # 权重
act: "softmax" # 激活函数，对输入使用激活函数处理，可以为softmax, sigmoid或者为None，默认为None
model_name_pairs: # 用于计算DML loss的子网络名称对，如果希望计算其他子网络的DML loss，可以在列表下面继续填充
- ["Student", "Teacher"]
key: head_out # 取子网络输出dict中，该key对应的tensor
- DistillationDistanceLoss: # 蒸馏的距离损失函数
weight: 1.0 # 权重
mode: "l2" # 距离计算方法，目前支持l1, l2, smooth_l1
model_name_pairs: # 用于计算distance loss的子网络名称对
- ["Student", "Teacher"]
key: backbone_out # 取子网络输出dict中，该key对应的tensor
```
上述损失函数中，所有的蒸馏损失函数均继承自标准的损失函数类，主要功能为: 对蒸馏模型的输出进行解析，找到用于计算损失的中间节点(tensor)，再使用标准的损失函数类去计算。
以上述配置为例，最终蒸馏训练的损失函数包含下面3个部分。
- `Student`和`Teacher`的最终输出(`head_out`)与gt的CTC loss，权重为1。在这里因为2个子网络都需要更新参数，因此2者都需要计算与g的loss。
- `Student`和`Teacher`的最终输出(`head_out`)之间的DML loss，权重为1。
- `Student`和`Teacher`的骨干网络输出(`backbone_out`)之间的l2 loss，权重为1。
关于`CombinedLoss`更加具体的实现可以参考: [combined_loss.py](../../ppocr/losses/combined_loss.py#L23)。关于`DistillationCTCLoss`等蒸馏损失函数更加具体的实现可以参考[distillation_loss.py](../../ppocr/losses/distillation_loss.py)。
#### 2.1.3 后处理
知识蒸馏任务中，后处理配置如下所示。
```yaml
PostProcess:
name: DistillationCTCLabelDecode # 蒸馏任务的CTC解码后处理，继承自标准的CTCLabelDecode类
model_name: ["Student", "Teacher"] # 对于蒸馏模型的预测结果，提取这两个子网络的输出，进行解码
key: head_out # 取子网络输出dict中，该key对应的tensor
```
以上述配置为例，最终会同时计算`Student`和`Teahcer` 2个子网络的CTC解码输出，返回一个`dict`，`key`为用于处理的子网络名称，`value`为用于处理的子网络列表。
关于`DistillationCTCLabelDecode`更加具体的实现可以参考: [rec_postprocess.py](../../ppocr/postprocess/rec_postprocess.py#L128)
#### 2.1.4 指标计算
知识蒸馏任务中，指标计算配置如下所示。
```yaml
Metric:
name: DistillationMetric # 蒸馏任务的CTC解码后处理，继承自标准的CTCLabelDecode类
base_metric_name: RecMetric # 指标计算的基类，对于模型的输出，会基于该类，计算指标
main_indicator: acc # 指标的名称
key: "Student" # 选取该子网络的 main_indicator 作为作为保存保存best model的判断标准
```
以上述配置为例，最终会使用`Student`子网络的acc指标作为保存best model的判断指标，同时，日志中也会打印出所有子网络的acc指标。
关于`DistillationMetric`更加具体的实现可以参考: [distillation_metric.py](../../ppocr/metrics/distillation_metric.py#L24)。
### 2.2 检测配置文件解析
* coming soon!
ppocr/data/imaug/label_ops.py
浏览文件 @ 6048ea5c
...@@ -19,6 +19,7 @@ from __future__ import unicode_literals ...@@ -19,6 +19,7 @@ from __future__ import unicode_literals
import numpy as np import numpy as np
import string import string
import json
class ClsLabelEncode(object): class ClsLabelEncode(object):
...@@ -39,7 +40,6 @@ class DetLabelEncode(object): ...@@ -39,7 +40,6 @@ class DetLabelEncode(object):
pass pass
def __call__(self, data): def __call__(self, data):
import json
label = data['label'] label = data['label']
label = json.loads(label) label = json.loads(label)
nBox = len(label) nBox = len(label)
...@@ -53,6 +53,8 @@ class DetLabelEncode(object): ...@@ -53,6 +53,8 @@ class DetLabelEncode(object):
txt_tags.append(True) txt_tags.append(True)
else: else:
txt_tags.append(False) txt_tags.append(False)
if len(boxes) == 0:
return None
boxes = self.expand_points_num(boxes) boxes = self.expand_points_num(boxes)
boxes = np.array(boxes, dtype=np.float32) boxes = np.array(boxes, dtype=np.float32)
txt_tags = np.array(txt_tags, dtype=np.bool) txt_tags = np.array(txt_tags, dtype=np.bool)
...@@ -352,19 +354,22 @@ class SRNLabelEncode(BaseRecLabelEncode): ...@@ -352,19 +354,22 @@ class SRNLabelEncode(BaseRecLabelEncode):
% beg_or_end % beg_or_end
return idx return idx
class TableLabelEncode(object): class TableLabelEncode(object):
""" Convert between text-label and text-index """ """ Convert between text-label and text-index """
def __init__(self,
max_text_length, def __init__(self,
max_elem_length, max_text_length,
max_cell_num, max_elem_length,
character_dict_path, max_cell_num,
span_weight = 1.0, character_dict_path,
**kwargs): span_weight=1.0,
**kwargs):
self.max_text_length = max_text_length self.max_text_length = max_text_length
self.max_elem_length = max_elem_length self.max_elem_length = max_elem_length
self.max_cell_num = max_cell_num self.max_cell_num = max_cell_num
list_character, list_elem = self.load_char_elem_dict(character_dict_path) list_character, list_elem = self.load_char_elem_dict(
character_dict_path)
list_character = self.add_special_char(list_character) list_character = self.add_special_char(list_character)
list_elem = self.add_special_char(list_elem) list_elem = self.add_special_char(list_elem)
self.dict_character = {} self.dict_character = {}
...@@ -374,7 +379,7 @@ class TableLabelEncode(object): ...@@ -374,7 +379,7 @@ class TableLabelEncode(object):
for i, elem in enumerate(list_elem): for i, elem in enumerate(list_elem):
self.dict_elem[elem] = i self.dict_elem[elem] = i
self.span_weight = span_weight self.span_weight = span_weight
def load_char_elem_dict(self, character_dict_path): def load_char_elem_dict(self, character_dict_path):
list_character = [] list_character = []
list_elem = [] list_elem = []
...@@ -383,27 +388,28 @@ class TableLabelEncode(object): ...@@ -383,27 +388,28 @@ class TableLabelEncode(object):
substr = lines[0].decode('utf-8').strip("\r\n").split("\t") substr = lines[0].decode('utf-8').strip("\r\n").split("\t")
character_num = int(substr[0]) character_num = int(substr[0])
elem_num = int(substr[1]) elem_num = int(substr[1])
for cno in range(1, 1+character_num):
for cno in range(1, 1 + character_num):
character = lines[cno].decode('utf-8').strip("\r\n") character = lines[cno].decode('utf-8').strip("\r\n")
list_character.append(character) list_character.append(character)
for eno in range(1+character_num, 1+character_num+elem_num): for eno in range(1 + character_num, 1 + character_num + elem_num):
elem = lines[eno].decode('utf-8').strip("\r\n") elem = lines[eno].decode('utf-8').strip("\r\n")
list_elem.append(elem) list_elem.append(elem)
return list_character, list_elem return list_character, list_elem
def add_special_char(self, list_character): def add_special_char(self, list_character):
self.beg_str = "sos" self.beg_str = "sos"
self.end_str = "eos" self.end_str = "eos"
list_character = [self.beg_str] + list_character + [self.end_str] list_character = [self.beg_str] + list_character + [self.end_str]
return list_character return list_character
def get_span_idx_list(self): def get_span_idx_list(self):
span_idx_list = [] span_idx_list = []
for elem in self.dict_elem: for elem in self.dict_elem:
if 'span' in elem: if 'span' in elem:
span_idx_list.append(self.dict_elem[elem]) span_idx_list.append(self.dict_elem[elem])
return span_idx_list return span_idx_list
def __call__(self, data): def __call__(self, data):
cells = data['cells'] cells = data['cells']
structure = data['structure']['tokens'] structure = data['structure']['tokens']
...@@ -412,18 +418,22 @@ class TableLabelEncode(object): ...@@ -412,18 +418,22 @@ class TableLabelEncode(object):
return None return None
elem_num = len(structure) elem_num = len(structure)
structure = [0] + structure + [len(self.dict_elem) - 1] structure = [0] + structure + [len(self.dict_elem) - 1]
structure = structure + [0] * (self.max_elem_length + 2 - len(structure)) structure = structure + [0] * (self.max_elem_length + 2 - len(structure)
)
structure = np.array(structure) structure = np.array(structure)
data['structure'] = structure data['structure'] = structure
elem_char_idx1 = self.dict_elem['<td>'] elem_char_idx1 = self.dict_elem['<td>']
elem_char_idx2 = self.dict_elem['<td'] elem_char_idx2 = self.dict_elem['<td']
span_idx_list = self.get_span_idx_list() span_idx_list = self.get_span_idx_list()
td_idx_list = np.logical_or(structure == elem_char_idx1, structure == elem_char_idx2) td_idx_list = np.logical_or(structure == elem_char_idx1,
structure == elem_char_idx2)
td_idx_list = np.where(td_idx_list)[0] td_idx_list = np.where(td_idx_list)[0]
structure_mask = np.ones((self.max_elem_length + 2, 1), dtype=np.float32) structure_mask = np.ones(
(self.max_elem_length + 2, 1), dtype=np.float32)
bbox_list = np.zeros((self.max_elem_length + 2, 4), dtype=np.float32) bbox_list = np.zeros((self.max_elem_length + 2, 4), dtype=np.float32)
bbox_list_mask = np.zeros((self.max_elem_length + 2, 1), dtype=np.float32) bbox_list_mask = np.zeros(
(self.max_elem_length + 2, 1), dtype=np.float32)
img_height, img_width, img_ch = data['image'].shape img_height, img_width, img_ch = data['image'].shape
if len(span_idx_list) > 0: if len(span_idx_list) > 0:
span_weight = len(td_idx_list) * 1.0 / len(span_idx_list) span_weight = len(td_idx_list) * 1.0 / len(span_idx_list)
...@@ -450,9 +460,11 @@ class TableLabelEncode(object): ...@@ -450,9 +460,11 @@ class TableLabelEncode(object):
char_end_idx = self.get_beg_end_flag_idx('end', 'char') char_end_idx = self.get_beg_end_flag_idx('end', 'char')
elem_beg_idx = self.get_beg_end_flag_idx('beg', 'elem') elem_beg_idx = self.get_beg_end_flag_idx('beg', 'elem')
elem_end_idx = self.get_beg_end_flag_idx('end', 'elem') elem_end_idx = self.get_beg_end_flag_idx('end', 'elem')
data['sp_tokens'] = np.array([char_beg_idx, char_end_idx, elem_beg_idx, data['sp_tokens'] = np.array([
elem_end_idx, elem_char_idx1, elem_char_idx2, self.max_text_length, char_beg_idx, char_end_idx, elem_beg_idx, elem_end_idx,
self.max_elem_length, self.max_cell_num, elem_num]) elem_char_idx1, elem_char_idx2, self.max_text_length,
self.max_elem_length, self.max_cell_num, elem_num
])
return data return data
def encode(self, text, char_or_elem): def encode(self, text, char_or_elem):
...@@ -504,9 +516,8 @@ class TableLabelEncode(object): ...@@ -504,9 +516,8 @@ class TableLabelEncode(object):
idx = np.array(self.dict_elem[self.end_str]) idx = np.array(self.dict_elem[self.end_str])
else: else:
assert False, "Unsupport type %s in get_beg_end_flag_idx of elem" \ assert False, "Unsupport type %s in get_beg_end_flag_idx of elem" \
% beg_or_end % beg_or_end
else: else:
assert False, "Unsupport type %s in char_or_elem" \ assert False, "Unsupport type %s in char_or_elem" \
% char_or_elem % char_or_elem
return idx return idx
\ No newline at end of file
ppstructure/README.md
浏览文件 @ 6048ea5c
...@@ -3,12 +3,11 @@ English | [简体中文](README_ch.md) ...@@ -3,12 +3,11 @@ English | [简体中文](README_ch.md)
# PP-Structure # PP-Structure
PP-Structure is an OCR toolkit that can be used for complex documents analysis. The main features are as follows: PP-Structure is an OCR toolkit that can be used for complex documents analysis. The main features are as follows:
- Support the layout analysis of documents, divide the documents into 5 types of areas **text, title, table, image and list** (conjunction with Layout-Parser) - Support the layout analysis of documents, divide the documents into 5 types of areas **text, title, table, image and list** (combined with Layout-Parser)
- Support to extract the texts from the text, title, picture and list areas (used in conjunction with PP-OCR) - Support to extract the texts from the text, title, picture and list areas (combined with PP-OCR)
- Support to extract excel files from the table areas - Support to extract excel files from the table areas
- Support python whl package and command line usage, easy to use - Support to use with python whl package and command line easily
- Support custom training for layout analysis and table structure tasks - Support custom training for layout analysis and table structure tasks
- The total model size is only about 18.6M (continuous optimization)
## 1. Visualization ## 1. Visualization
...@@ -187,4 +186,4 @@ OCR and table recognition model ...@@ -187,4 +186,4 @@ 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) | |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) |
|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) | |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) |
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` . 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` .
\ No newline at end of file
ppstructure/README_ch.md
浏览文件 @ 6048ea5c
...@@ -8,7 +8,6 @@ PP-Structure是一个可用于复杂文档结构分析和处理的OCR工具包 ...@@ -8,7 +8,6 @@ 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 - 支持版面分析和表格结构化两类任务自定义训练
- 总模型大小仅有18.6M（持续优化）
## 1. 效果展示 ## 1. 效果展示
......
requirements.txt
浏览文件 @ 6048ea5c
...@@ -10,4 +10,4 @@ python-Levenshtein ...@@ -10,4 +10,4 @@ python-Levenshtein
opencv-contrib-python==4.2.0.32 opencv-contrib-python==4.2.0.32
lxml lxml
premailer premailer
openpyxl openpyxl
\ No newline at end of file
tools/infer/utility.py
浏览文件 @ 6048ea5c
...@@ -24,6 +24,7 @@ from paddle import inference ...@@ -24,6 +24,7 @@ from paddle import inference
import time import time
from ppocr.utils.logging import get_logger from ppocr.utils.logging import get_logger
def str2bool(v): def str2bool(v):
return v.lower() in ("true", "t", "1") return v.lower() in ("true", "t", "1")
...@@ -47,8 +48,8 @@ def init_args(): ...@@ -47,8 +48,8 @@ def init_args():
# DB parmas # DB parmas
parser.add_argument("--det_db_thresh", type=float, default=0.3) parser.add_argument("--det_db_thresh", type=float, default=0.3)
parser.add_argument("--det_db_box_thresh", type=float, default=0.5) parser.add_argument("--det_db_box_thresh", type=float, default=0.6)
parser.add_argument("--det_db_unclip_ratio", type=float, default=1.6) parser.add_argument("--det_db_unclip_ratio", type=float, default=1.5)
parser.add_argument("--max_batch_size", type=int, default=10) parser.add_argument("--max_batch_size", type=int, default=10)
parser.add_argument("--use_dilation", type=bool, default=False) parser.add_argument("--use_dilation", type=bool, default=False)
parser.add_argument("--det_db_score_mode", type=str, default="fast") parser.add_argument("--det_db_score_mode", type=str, default="fast")
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或