Skip to content

P
PaddleOCR

PaddlePaddle / PaddleOCR
大约 1 年 前同步成功

通知 1528
Star 32962
Fork 6643
P
PaddleOCR
未验证 提交 40065d0f 编写于 作者: A andyj 提交者： GitHub
浏览文件
操作

Merge pull request #7879 from WenmuZhou/tipc_2 

[TIPC] add kl and pact for slanet and vi-layoutxlm
上级 9df7730e aa7c0054
隐藏空白更改
内联 并排
Showing with 328 addition and 5 deletion
ppstructure/docs/quickstart.md
浏览文件 @ 40065d0f
......@@ -214,7 +214,7 @@ for line in result:
<a name="225"></a>
#### 2.2.5 关键信息抽取
关键信息抽取暂不支持通过whl包调用，详细使用教程请参考：[关键信息抽取教程](../kie/README_ch.md)。
关键信息抽取暂不支持通过whl包调用，详细使用教程请参考：[inference文档](./inference.md)。
<a name="226"></a>
......
ppstructure/docs/quickstart_en.md
浏览文件 @ 40065d0f
......@@ -94,7 +94,7 @@ paddleocr --image_dir=ppstructure/docs/table/table.jpg --type=structure --layout
#### 2.1.5 Key Information Extraction
Key information extraction does not currently support use by the whl package. For detailed usage tutorials, please refer to: [Key Information Extraction](../kie/README.md).
Key information extraction does not currently support use by the whl package. For detailed usage tutorials, please refer to: [inference document](./inference_en.md).
<a name="216"></a>
#### 2.1.6 layout recovery
......
test_tipc/configs/slanet/SLANet.yml
浏览文件 @ 40065d0f
......@@ -12,7 +12,7 @@ Global:
checkpoints:
save_inference_dir: ./output/SLANet/infer
use_visualdl: False
infer_img: doc/table/table.jpg
infer_img: ppstructure/docs/table/table.jpg
# for data or label process
character_dict_path: ppocr/utils/dict/table_structure_dict.txt
character_type: en
......
test_tipc/configs/slanet/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt 0 → 100644
浏览文件 @ 40065d0f
===========================train_params===========================
model_name:slanet
python:python3.7
gpu_list:0|0,1
Global.use_gpu:True|True
Global.auto_cast:amp
Global.epoch_num:lite_train_lite_infer=3|whole_train_whole_infer=50
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=128
Global.pretrained_model:./pretrain_models/en_ppstructure_mobile_v2.0_SLANet_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/slanet/SLANet.yml -o
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/slanet/SLANet.yml -o
quant_export:
fpgm_export:
distill_export:null
export1:null
export2:null
##
infer_model:./inference/en_ppstructure_mobile_v2.0_SLANet_train
infer_export:null
infer_quant:False
inference:ppstructure/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 --rec_char_dict_path=./ppocr/utils/dict/table_dict.txt --table_char_dict_path=./ppocr/utils/dict/table_structure_dict.txt --image_dir=./ppstructure/docs/table/table.jpg --det_limit_side_len=736 --det_limit_type=min --output ./output/table
--use_gpu:True|False
--enable_mkldnn:False
--cpu_threads:6
--rec_batch_num:1
--use_tensorrt:True
--precision:fp32
--table_model_dir:
--image_dir:./ppstructure/docs/table/table.jpg
null:null
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,488,488]}]
test_tipc/configs/slanet/train_pact_infer_python.txt 0 → 100644
浏览文件 @ 40065d0f
===========================train_params===========================
model_name:slanet_PACT
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=50
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=2
Global.pretrained_model:./pretrain_models/en_ppstructure_mobile_v2.0_SLANet_train/best_accuracy
train_model_name:latest
train_infer_img_dir:./ppstructure/docs/table/table.jpg
null:null
##
trainer:pact_train
norm_train:null
pact_train:deploy/slim/quantization/quant.py -c test_tipc/configs/slanet/SLANet.yml -o
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:null
quant_export:deploy/slim/quantization/export_model.py -c test_tipc/configs/slanet/SLANet.yml -o
fpgm_export:
distill_export:null
export1:null
export2:null
##
infer_model:./inference/en_ppocr_mobile_v2.0_table_structure_infer
infer_export:null
infer_quant:True
inference:ppstructure/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 --rec_char_dict_path=./ppocr/utils/dict/table_dict.txt --table_char_dict_path=./ppocr/utils/dict/table_structure_dict.txt --image_dir=./ppstructure/docs/table/table.jpg --det_limit_side_len=736 --det_limit_type=min --output ./output/table
--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:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,488,488]}]
test_tipc/configs/slanet/train_ptq_infer_python.txt 0 → 100644
浏览文件 @ 40065d0f
===========================train_params===========================
model_name:slanet_KL
python:python3.7
Global.pretrained_model:
Global.save_inference_dir:null
infer_model:./inference/en_ppstructure_mobile_v2.0_SLANet_infer/
infer_export:deploy/slim/quantization/quant_kl.py -c test_tipc/configs/slanet/SLANet.yml -o
infer_quant:True
inference:ppstructure/table/predict_table.py --det_model_dir=./inference/ch_PP-OCRv3_det_infer --rec_model_dir=./inference/ch_PP-OCRv3_rec_infer --rec_char_dict_path=./ppocr/utils/ppocr_keys_v1.txt --table_char_dict_path=./ppocr/utils/dict/table_structure_dict.txt --image_dir=./ppstructure/docs/table/table.jpg --det_limit_side_len=736 --det_limit_type=min --output ./output/table
--use_gpu:True|False
--enable_mkldnn:False
--cpu_threads:6
--rec_batch_num:1
--use_tensorrt:False
--precision:int8
--table_model_dir:
--image_dir:./ppstructure/docs/table/table.jpg
null:null
--benchmark:True
null:null
null:null
test_tipc/configs/vi_layoutxlm_ser/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt 0 → 100644
浏览文件 @ 40065d0f
===========================train_params===========================
model_name:vi_layoutxlm_ser
python:python3.7
gpu_list:192.168.0.1,192.168.0.2;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=4|whole_train_whole_infer=8
Architecture.Backbone.checkpoints:null
train_model_name:latest
train_infer_img_dir:ppstructure/docs/kie/input/zh_val_42.jpg
null:null
##
trainer:norm_train
norm_train:tools/train.py -c ./configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o
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/
Architecture.Backbone.checkpoints:
norm_export:tools/export_model.py -c ./configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o
quant_export:
fpgm_export:
distill_export:null
export1:null
export2:null
##
infer_model:null
infer_export:null
infer_quant:False
inference:ppstructure/kie/predict_kie_token_ser.py --kie_algorithm=LayoutXLM --ser_dict_path=train_data/XFUND/class_list_xfun.txt --output=output --ocr_order_method=tb-yx
--use_gpu:True|False
--enable_mkldnn:False
--cpu_threads:6
--rec_batch_num:1
--use_tensorrt:False
--precision:fp32
--ser_model_dir:
--image_dir:./ppstructure/docs/kie/input/zh_val_42.jpg
null:null
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,224,224]}]
test_tipc/configs/vi_layoutxlm_ser/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt 0 → 100644
浏览文件 @ 40065d0f
===========================train_params===========================
model_name:vi_layoutxlm_ser
python:python3.7
gpu_list:0|0,1
Global.use_gpu:True|True
Global.auto_cast:amp
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=4|whole_train_whole_infer=8
Architecture.Backbone.checkpoints:null
train_model_name:latest
train_infer_img_dir:ppstructure/docs/kie/input/zh_val_42.jpg
null:null
##
trainer:norm_train
norm_train:tools/train.py -c ./configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o
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/
Architecture.Backbone.checkpoints:
norm_export:tools/export_model.py -c ./configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o
quant_export:
fpgm_export:
distill_export:null
export1:null
export2:null
##
infer_model:null
infer_export:null
infer_quant:False
inference:ppstructure/kie/predict_kie_token_ser.py --kie_algorithm=LayoutXLM --ser_dict_path=train_data/XFUND/class_list_xfun.txt --output=output --ocr_order_method=tb-yx
--use_gpu:True|False
--enable_mkldnn:False
--cpu_threads:6
--rec_batch_num:1
--use_tensorrt:False
--precision:fp32
--ser_model_dir:
--image_dir:./ppstructure/docs/kie/input/zh_val_42.jpg
null:null
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,224,224]}]
test_tipc/configs/vi_layoutxlm_ser/train_pact_infer_python.txt 0 → 100644
浏览文件 @ 40065d0f
===========================train_params===========================
model_name:vi_layoutxlm_ser_PACT
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=4|whole_train_whole_infer=8
Architecture.Backbone.pretrained:./pretrain_models/ser_vi_layoutxlm_xfund_pretrained/best_accuracy
train_model_name:latest
train_infer_img_dir:ppstructure/docs/kie/input/zh_val_42.jpg
null:null
##
trainer:pact_train
norm_train:null
pact_train:deploy/slim/quantization/quant.py -c ./configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o Global.eval_batch_step=[2000,10]
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:null
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Architecture.Backbone.checkpoints:
norm_export:null
quant_export:deploy/slim/quantization/export_model.py -c ./configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o
fpgm_export: null
distill_export:null
export1:null
export2:null
##
infer_model:null
infer_export:null
infer_quant:False
inference:ppstructure/kie/predict_kie_token_ser.py --kie_algorithm=LayoutXLM --ser_dict_path=train_data/XFUND/class_list_xfun.txt --output=output --ocr_order_method=tb-yx
--use_gpu:True|False
--enable_mkldnn:False
--cpu_threads:6
--rec_batch_num:1
--use_tensorrt:False
--precision:fp32
--ser_model_dir:
--image_dir:./ppstructure/docs/kie/input/zh_val_42.jpg
null:null
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,224,224]}]
test_tipc/configs/vi_layoutxlm_ser/train_ptq_infer_python.txt 0 → 100644
浏览文件 @ 40065d0f
===========================train_params===========================
model_name:vi_layoutxlm_ser_KL
python:python3.7
Global.pretrained_model:
Global.save_inference_dir:null
infer_model:./inference/ser_vi_layoutxlm_xfund_infer/
infer_export:deploy/slim/quantization/quant_kl.py -c ./configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o Train.loader.batch_size_per_card=1 Eval.loader.batch_size_per_card=1
infer_quant:True
inference:ppstructure/kie/predict_kie_token_ser.py --kie_algorithm=LayoutXLM --ser_dict_path=train_data/XFUND/class_list_xfun.txt --output=output --ocr_order_method=tb-yx
--use_gpu:True|False
--enable_mkldnn:False
--cpu_threads:6
--rec_batch_num:1
--use_tensorrt:False
--precision:int8
--ser_model_dir:
--image_dir:./ppstructure/docs/kie/input/zh_val_42.jpg
null:null
--benchmark:True
null:null
null:null
test_tipc/prepare.sh
浏览文件 @ 40065d0f
......@@ -164,7 +164,7 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar --no-check-certificate
cd ./inference/ && tar xf en_ppocr_mobile_v2.0_table_det_infer.tar && tar xf en_ppocr_mobile_v2.0_table_rec_infer.tar && cd ../
fi
if [ ${model_name} == "slanet" ];then
if [[ ${model_name} =~ "slanet" ]];then
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/en_ppstructure_mobile_v2.0_SLANet_train.tar --no-check-certificate
cd ./pretrain_models/ && tar xf en_ppstructure_mobile_v2.0_SLANet_train.tar && cd ../
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar --no-check-certificate
......@@ -267,12 +267,16 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar --no-check-certificate
cd ./pretrain_models/ && tar xf ser_LayoutXLM_xfun_zh.tar && cd ../
fi
if [ ${model_name} == "vi_layoutxlm_ser" ]; then
if [[ ${model_name} =~ "vi_layoutxlm_ser" ]]; then
${python_name} -m pip install -r ppstructure/kie/requirements.txt
${python_name} -m pip install opencv-python -U
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/ppstructure/dataset/XFUND.tar --no-check-certificate
cd ./train_data/ && tar xf XFUND.tar
cd ../
if [ ${model_name} == "vi_layoutxlm_ser_PACT" ]; then
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_pretrained.tar --no-check-certificate
cd ./pretrain_models/ && tar xf ser_vi_layoutxlm_xfund_pretrained.tar && cd ../
fi
fi
if [ ${model_name} == "det_r18_ct" ]; then
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/ResNet18_vd_pretrained.pdparams --no-check-certificate
......@@ -532,6 +536,18 @@ elif [ ${MODE} = "whole_infer" ];then
fi
cd ../
fi
if [[ ${model_name} =~ "slanet" ]];then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/en_ppstructure_mobile_v2.0_SLANet_infer.tar --no-check-certificate
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar --no-check-certificate
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar --no-check-certificate
cd ./inference/ && tar xf en_ppstructure_mobile_v2.0_SLANet_infer.tar && tar xf ch_PP-OCRv3_det_infer.tar && tar xf ch_PP-OCRv3_rec_infer.tar && cd ../
fi
if [[ ${model_name} =~ "vi_layoutxlm_ser" ]]; then
${python_name} -m pip install -r ppstructure/kie/requirements.txt
${python_name} -m pip install opencv-python -U
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_infer.tar --no-check-certificate
cd ./inference/ && tar xf ser_vi_layoutxlm_xfund_infer.tar & cd ../
fi
if [[ ${model_name} =~ "layoutxlm_ser" ]]; then
${python_name} -m pip install -r ppstructure/kie/requirements.txt
${python_name} -m pip install opencv-python -U
......
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