mirrors
/
PaddleOCR
mirror of https://github.com/PaddlePaddle/PaddleOCR.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

add tablemaster to tipc

pull/6795/head
WenmuZhou 2022-07-05 09:31:53 +00:00
parent 52d5b54569
commit 27181149a9
10 changed files with 237 additions and 32 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
doc/doc_ch/algorithm.md
View File

@ -5,9 +5,10 @@ PaddleOCR将**持续新增**支持OCR领域前沿算法与模型，已支持的
- [文本检测算法](./algorithm_overview.md#11-%E6%96%87%E6%9C%AC%E6%A3%80%E6%B5%8B%E7%AE%97%E6%B3%95)
- [文本识别算法](./algorithm_overview.md#12-%E6%96%87%E6%9C%AC%E8%AF%86%E5%88%AB%E7%AE%97%E6%B3%95)
- [端到端算法](./algorithm_overview.md#2-%E6%96%87%E6%9C%AC%E8%AF%86%E5%88%AB%E7%AE%97%E6%B3%95)
- [表格识别]](./algorithm_overview.md#3-%E8%A1%A8%E6%A0%BC%E8%AF%86%E5%88%AB%E7%AE%97%E6%B3%95)
**欢迎广大开发者合作共建，贡献更多算法，合入有奖🎁！具体可查看[社区常规赛](https://github.com/PaddlePaddle/PaddleOCR/issues/4982)。**
新增算法可参考如下教程：
- [使用PaddleOCR架构添加新算法](./add_new_algorithm.md)
- [使用PaddleOCR架构添加新算法](./add_new_algorithm.md)

12
doc/doc_ch/algorithm_overview.md
View File

@ -1,10 +1,10 @@
# OCR算法
- [1. 两阶段算法](#1-两阶段算法)
- [1.1 文本检测算法](#11-文本检测算法)
- [1.2 文本识别算法](#12-文本识别算法)
- [2. 端到端算法](#2-端到端算法)
- [3. 表格识别算法](#3-表格识别算法)
- [1. 两阶段算法](#1)
- [1.1 文本检测算法](#11)
- [1.2 文本识别算法](#12)
- [2. 端到端算法](#2)
- [3. 表格识别算法](#3)
本文给出了PaddleOCR已支持的OCR算法列表，以及每个算法在**英文公开数据集**上的模型和指标，主要用于算法简介和算法性能对比，更多包括中文在内的其他数据集上的模型请参考[PP-OCR v2.0 系列模型下载](./models_list.md)。
@ -98,6 +98,8 @@
已支持的端到端OCR算法列表（戳链接获取使用教程）：
- [x] [PGNet](./algorithm_e2e_pgnet.md)
<a name="3"></a>
## 3. 表格识别算法
已支持的表格识别算法列表（戳链接获取使用教程）：

3
doc/doc_en/algorithm_en.md
View File

@ -6,5 +6,6 @@ PaddleOCR will add cutting-edge OCR algorithms and models continuously. Check ou
- [text detection algorithms](./algorithm_overview_en.md#11)
- [text recognition algorithms](./algorithm_overview_en.md#12)
- [end-to-end algorithms](./algorithm_overview_en.md#2)
- [table recognition algorithms](./algorithm_overview_en.md#3)
Developers are welcome to contribute more algorithms! Please refer to [add new algorithm](./add_new_algorithm_en.md) guideline.
Developers are welcome to contribute more algorithms! Please refer to [add new algorithm](./add_new_algorithm_en.md) guideline.

10
doc/doc_en/algorithm_overview_en.md
View File

@ -1,10 +1,10 @@
# OCR Algorithms
- [1. Two-stage Algorithms](#1-two-stage-algorithms)
- [1.1 Text Detection Algorithms](#11-text-detection-algorithms)
- [1.2 Text Recognition Algorithms](#12-text-recognition-algorithms)
- [2. End-to-end Algorithms](#2-end-to-end-algorithms)
- [3. Table Recognition Algorithms](#3-table-recognition-algorithms)
- [1. Two-stage Algorithms](#1)
- [1.1 Text Detection Algorithms](#11)
- [1.2 Text Recognition Algorithms](#12)
- [2. End-to-end Algorithms](#2)
- [3. Table Recognition Algorithms](#3)
This tutorial lists the OCR algorithms supported by PaddleOCR, as well as the models and metrics of each algorithm on **English public datasets**. It is mainly used for algorithm introduction and algorithm performance comparison. For more models on other datasets including Chinese, please refer to [PP-OCR v2.0 models list](./models_list_en.md).

6
ppstructure/table/predict_structure.py
View File

@ -118,7 +118,7 @@ class TableStructurer(object):
'<html>', '<body>', '<table>'
] + structure_str_list + ['</table>', '</body>', '</html>']
elapse = time.time() - starttime
return structure_str_list, bbox_list, elapse
return (structure_str_list, bbox_list), elapse
def main(args):
@ -138,8 +138,8 @@ def main(args):
if img is None:
logger.info("error in loading image:{}".format(image_file))
continue
structure_str_list, bbox_list, elapse = table_structurer(img)
structure_res, elapse = table_structurer(img)
structure_str_list, bbox_list = structure_res
bbox_list_str = json.dumps(bbox_list.tolist())
logger.info("result: {}, {}".format(structure_str_list,
bbox_list_str))

41
test_tipc/configs/en_table_structure/table_mv3.yml
View File

@ -1,24 +1,23 @@
Global:
use_gpu: true
epoch_num: 10
epoch_num: 400
log_smooth_window: 20
print_batch_step: 5
save_model_dir: ./output/table_mv3/
save_epoch_step: 3
save_epoch_step: 400
# evaluation is run every 400 iterations after the 0th iteration
eval_batch_step: [0, 400]
cal_metric_during_train: True
pretrained_model:
checkpoints:
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/table/table.jpg
infer_img: ppstructure/docs/table/table.jpg
save_res_path: output/table_mv3
# for data or label process
character_dict_path: ppocr/utils/dict/table_structure_dict.txt
character_type: en
max_text_length: 100
max_elem_length: 800
max_cell_num: 500
max_text_length: 800
infer_mode: False
process_total_num: 0
process_cut_num: 0
@ -44,11 +43,8 @@ Architecture:
Head:
name: TableAttentionHead
hidden_size: 256
l2_decay: 0.00001
loc_type: 2
max_text_length: 100
max_elem_length: 800
max_cell_num: 500
max_text_length: 800
Loss:
name: TableAttentionLoss
@ -61,28 +57,34 @@ PostProcess:
Metric:
name: TableMetric
main_indicator: acc
compute_bbox_metric: false # cost many time, set False for training
Train:
dataset:
name: PubTabDataSet
data_dir: ./train_data/pubtabnet/train
label_file_path: ./train_data/pubtabnet/train.jsonl
label_file_list: [./train_data/pubtabnet/train.jsonl]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- TableLabelEncode:
learn_empty_box: False
merge_no_span_structure: False
replace_empty_cell_token: False
- TableBoxEncode:
- ResizeTableImage:
max_len: 488
- TableLabelEncode:
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- PaddingTableImage:
size: [488, 488]
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'structure', 'bbox_list', 'sp_tokens', 'bbox_list_mask']
keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ]
loader:
shuffle: True
batch_size_per_card: 32
@ -93,23 +95,28 @@ Eval:
dataset:
name: PubTabDataSet
data_dir: ./train_data/pubtabnet/test/
label_file_path: ./train_data/pubtabnet/test.jsonl
label_file_list: [./train_data/pubtabnet/test.jsonl]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- TableLabelEncode:
learn_empty_box: False
merge_no_span_structure: False
replace_empty_cell_token: False
- TableBoxEncode:
- ResizeTableImage:
max_len: 488
- TableLabelEncode:
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- PaddingTableImage:
size: [488, 488]
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'structure', 'bbox_list', 'sp_tokens', 'bbox_list_mask']
keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ]
loader:
shuffle: False
drop_last: False

136
test_tipc/configs/table_master/table_master.yml 100644
View File

@ -0,0 +1,136 @@
Global:
use_gpu: true
epoch_num: 17
log_smooth_window: 20
print_batch_step: 100
save_model_dir: ./output/table_master/
save_epoch_step: 17
eval_batch_step: [0, 6259]
cal_metric_during_train: true
pretrained_model: null
checkpoints:
save_inference_dir: output/table_master/infer
use_visualdl: false
infer_img: ppstructure/docs/table/table.jpg
save_res_path: ./output/table_master
character_dict_path: ppocr/utils/dict/table_master_structure_dict.txt
infer_mode: false
max_text_length: 500
process_total_num: 0
process_cut_num: 0
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: MultiStepDecay
learning_rate: 0.001
milestones: [12, 15]
gamma: 0.1
warmup_epoch: 0.02
regularizer:
name: L2
factor: 0.0
Architecture:
model_type: table
algorithm: TableMaster
Backbone:
name: TableResNetExtra
gcb_config:
ratio: 0.0625
headers: 1
att_scale: False
fusion_type: channel_add
layers: [False, True, True, True]
layers: [1,2,5,3]
Head:
name: TableMasterHead
hidden_size: 512
headers: 8
dropout: 0
d_ff: 2024
max_text_length: 500
Loss:
name: TableMasterLoss
ignore_index: 42 # set to len of dict + 3
PostProcess:
name: TableMasterLabelDecode
box_shape: pad
Metric:
name: TableMetric
main_indicator: acc
compute_bbox_metric: False
Train:
dataset:
name: PubTabDataSet
data_dir: ./train_data/pubtabnet/train
label_file_list: [./train_data/pubtabnet/train.jsonl]
transforms:
- DecodeImage:
img_mode: BGR
channel_first: False
- TableMasterLabelEncode:
learn_empty_box: False
merge_no_span_structure: True
replace_empty_cell_token: True
- ResizeTableImage:
max_len: 480
resize_bboxes: True
- PaddingTableImage:
size: [480, 480]
- TableBoxEncode:
use_xywh: True
- NormalizeImage:
scale: 1./255.
mean: [0.5, 0.5, 0.5]
std: [0.5, 0.5, 0.5]
order: hwc
- ToCHWImage: null
- KeepKeys:
keep_keys: [image, structure, bboxes, bbox_masks, shape]
loader:
shuffle: True
batch_size_per_card: 10
drop_last: True
num_workers: 8
Eval:
dataset:
name: PubTabDataSet
data_dir: ./train_data/pubtabnet/test/
label_file_list: [./train_data/pubtabnet/test.jsonl]
transforms:
- DecodeImage:
img_mode: BGR
channel_first: False
- TableMasterLabelEncode:
learn_empty_box: False
merge_no_span_structure: True
replace_empty_cell_token: True
- ResizeTableImage:
max_len: 480
resize_bboxes: True
- PaddingTableImage:
size: [480, 480]
- TableBoxEncode:
use_xywh: True
- NormalizeImage:
scale: 1./255.
mean: [0.5, 0.5, 0.5]
std: [0.5, 0.5, 0.5]
order: hwc
- ToCHWImage: null
- KeepKeys:
keep_keys: [image, structure, bboxes, bbox_masks, shape]
loader:
shuffle: False
drop_last: False
batch_size_per_card: 10
num_workers: 8

53
test_tipc/configs/table_master/train_infer_python.txt 100644
View File

@ -0,0 +1,53 @@
===========================train_params===========================
model_name:table_master
python:python3.7
gpu_list:0|0,1
Global.use_gpu:True|True
Global.auto_cast:fp32
Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=17
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
Global.pretrained_model:./pretrain_models/table_structure_tablemaster_train/best_accuracy
train_model_name:latest
train_infer_img_dir:./ppstructure/docs/table/table.jpg
null:null
##
trainer:norm_train
norm_train:tools/train.py -c test_tipc/configs/table_master/table_master.yml -o Global.print_batch_step=10
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:null
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.checkpoints:
norm_export:tools/export_model.py -c test_tipc/configs/table_master/table_master.yml -o
quant_export:
fpgm_export:
distill_export:null
export1:null
export2:null
##
infer_model:null
infer_export:null
infer_quant:False
inference:ppstructure/table/predict_structure.py --table_char_dict_path=./ppocr/utils/dict/table_master_structure_dict.txt --image_dir=./ppstructure/docs/table/table.jpg --output ./output/table --table_algorithm=TableMaster --table_max_len=480
--use_gpu:True|False
--enable_mkldnn:False
--cpu_threads:6
--rec_batch_num:1
--use_tensorrt:False
--precision:fp32
--table_model_dir:
--image_dir:./ppstructure/docs/table/table.jpg
null:null
--benchmark:False
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,480,480]}]

4
test_tipc/prepare.sh
View File

@ -62,6 +62,10 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
if [[ ${model_name} =~ "det_r50_db++" ]];then
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams --no-check-certificate
fi
if [ ${model_name} == "table_master" ];then
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar --no-check-certificate
cd ./pretrain_models/ && tar xf table_structure_tablemaster_train.tar && cd ../
fi
cd ./pretrain_models/ && tar xf det_mv3_db_v2.0_train.tar && cd ../
rm -rf ./train_data/icdar2015
rm -rf ./train_data/ic15_data

1
test_tipc/readme.md
View File

@ -54,6 +54,7 @@
| NRTR |rec_mtb_nrtr | 识别 | 支持 | 多机多卡 <br> 混合精度 | - | - |
| SAR |rec_r31_sar | 识别 | 支持 | 多机多卡 <br> 混合精度 | - | - |
| PGNet |rec_r34_vd_none_none_ctc_v2.0 | 端到端| 支持 | 多机多卡 <br> 混合精度 | - | - |
| TableMaster |table_structure_tablemaster_train | 表格识别| 支持 | 多机多卡 <br> 混合精度 | - | - |