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add layoutxml kl he pact

pull/7591/head
Wenmuzhou 2022-09-14 15:30:30 +08:00
parent 0c8ac23851
commit 541954da52
5 changed files with 125 additions and 10 deletions
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6
deploy/slim/quantization/quant.py
View File

@ -158,8 +158,7 @@ def main(config, device, logger, vdl_writer):
pre_best_model_dict = dict()
# load fp32 model to begin quantization
if config["Global"]["pretrained_model"] is not None:
pre_best_model_dict = load_model(config, model)
pre_best_model_dict = load_model(config, model, None, config['Architecture']["model_type"])
freeze_params = False
if config['Architecture']["algorithm"] in ["Distillation"]:
@ -184,8 +183,7 @@ def main(config, device, logger, vdl_writer):
model=model)
# resume PACT training process
if config["Global"]["checkpoints"] is not None:
pre_best_model_dict = load_model(config, model, optimizer)
pre_best_model_dict = load_model(config, model, optimizer, config['Architecture']["model_type"])
# build metric
eval_class = build_metric(config['Metric'])

24
deploy/slim/quantization/quant_kl.py
View File

@ -97,6 +97,17 @@ def sample_generator(loader):
return __reader__
def sample_generator_layoutxlm_ser(loader):
def __reader__():
for indx, data in enumerate(loader):
input_ids = np.array(data[0])
bbox = np.array(data[1])
attention_mask = np.array(data[2])
token_type_ids = np.array(data[3])
images = np.array(data[4])
yield [input_ids, bbox, attention_mask, token_type_ids, images]
return __reader__
def main(config, device, logger, vdl_writer):
# init dist environment
@ -107,16 +118,18 @@ def main(config, device, logger, vdl_writer):
# build dataloader
config['Train']['loader']['num_workers'] = 0
is_layoutxlm_ser = config['Architecture']['model_type'] =='kie' and config['Architecture']['Backbone']['name'] == 'LayoutXLMForSer'
train_dataloader = build_dataloader(config, 'Train', device, logger)
if config['Eval']:
config['Eval']['loader']['num_workers'] = 0
valid_dataloader = build_dataloader(config, 'Eval', device, logger)
if is_layoutxlm_ser:
train_dataloader = valid_dataloader
else:
valid_dataloader = None
paddle.enable_static()
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
exe = paddle.static.Executor(device)
if 'inference_model' in global_config.keys(): # , 'inference_model'):
inference_model_dir = global_config['inference_model']
@ -127,6 +140,11 @@ def main(config, device, logger, vdl_writer):
raise ValueError(
"Please set inference model dir in Global.inference_model or Global.pretrained_model for post-quantazition"
)
if is_layoutxlm_ser:
generator = sample_generator_layoutxlm_ser(train_dataloader)
else:
generator = sample_generator(train_dataloader)
paddleslim.quant.quant_post_static(
executor=exe,
@ -134,7 +152,7 @@ def main(config, device, logger, vdl_writer):
model_filename='inference.pdmodel',
params_filename='inference.pdiparams',
quantize_model_path=global_config['save_inference_dir'],
sample_generator=sample_generator(train_dataloader),
sample_generator=generator,
save_model_filename='inference.pdmodel',
save_params_filename='inference.pdiparams',
batch_size=1,

53
test_tipc/configs/layoutxlm_ser/train_pact_infer_python.txt 100644
View File

@ -0,0 +1,53 @@
===========================train_params===========================
model_name: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.checkpoints:pretrain_models/ser_LayoutXLM_xfun_zh
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 test_tipc/configs/layoutxlm_ser/ser_layoutxlm_xfund_zh.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/
Architecture.Backbone.checkpoints:
norm_export:null
quant_export:deploy/slim/quantization/export_model.py -c test_tipc/configs/layoutxlm_ser/ser_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
--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:False
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,224,224]}]

21
test_tipc/configs/layoutxlm_ser/train_ptq_infer_python.txt 100644
View File

@ -0,0 +1,21 @@
===========================train_params===========================
model_name:layoutxlm_ser_KL
python:python3.7
Global.pretrained_model:
Global.save_inference_dir:null
infer_model:./inference/ser_LayoutXLM_xfun_zh_infer/
infer_export:deploy/slim/quantization/quant_kl.py -c test_tipc/configs/layoutxlm_ser/ser_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
--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:False
null:null
null:null

31
test_tipc/prepare.sh
View File

@ -145,7 +145,7 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
array=(${python_name_list})
python_name=${array[0]}
${python_name} -m pip install -r requirements.txt
${python_name} -m pip install git+https://github.com/LDOUBLEV/AutoLog
${python_name} -m pip install https://paddleocr.bj.bcebos.com/libs/auto_log-1.2.0-py3-none-any.whl
# pretrain lite train data
wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams --no-check-certificate
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar --no-check-certificate
@ -257,7 +257,17 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/rec_r32_gaspin_bilstm_att_train.tar --no-check-certificate
cd ./pretrain_models/ && tar xf rec_r32_gaspin_bilstm_att_train.tar && cd ../
fi
if [ ${model_name} == "layoutxlm_ser" ] || [ ${model_name} == "vi_layoutxlm_ser" ]; then
if [ ${model_name} == "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 ../
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
${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
@ -332,9 +342,18 @@ elif [ ${MODE} = "lite_train_whole_infer" ];then
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
elif [ ${MODE} = "whole_infer" ];then
python_name_list=$(func_parser_value "${lines[2]}")
array=(${python_name_list})
python_name=${array[0]}
${python_name} -m pip install paddleslim
${python_name} -m pip install -r requirements.txt
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 && tar xf ch_det_data_50.tar && cd ../
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 ../
head -n 2 train_data/XFUND/zh_val/val.json > train_data/XFUND/zh_val/val_lite.json
mv train_data/XFUND/zh_val/val_lite.json train_data/XFUND/zh_val/val.json
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
@ -500,6 +519,12 @@ elif [ ${MODE} = "whole_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_structure_infer.tar && 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} =~ "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/pplayout/ser_LayoutXLM_xfun_zh_infer.tar --no-check-certificate
cd ./inference/ && tar xf ser_LayoutXLM_xfun_zh_infer.tar & cd ../
fi
fi
if [[ ${model_name} =~ "KL" ]]; then
@ -667,7 +692,7 @@ if [ ${MODE} = "serving_infer" ];then
${python_name} -m pip install paddle-serving-server-gpu
${python_name} -m pip install paddle_serving_client
${python_name} -m pip install paddle-serving-app
${python_name} -m pip install git+https://github.com/LDOUBLEV/AutoLog
${python_name} -m pip install https://paddleocr.bj.bcebos.com/libs/auto_log-1.2.0-py3-none-any.whl
# wget model
if [ ${model_name} == "ch_ppocr_mobile_v2_0_det_KL" ] || [ ${model_name} == "ch_ppocr_mobile_v2.0_rec_KL" ] ; then
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/tipc_fake_model/ch_ppocr_mobile_v2.0_det_klquant_infer.tar --no-check-certificate