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Commit 915d0632 authored by WenmuZhou's avatar WenmuZhou
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merge dygraph

parents cbc6de9a 829a393c
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Showing with 588 additions and 400 deletions
configs/rec/rec_icdar15_train.yml
View file @ 915d0632
...@@ -10,7 +10,7 @@ Global: ...@@ -10,7 +10,7 @@ Global:
cal_metric_during_train: True cal_metric_during_train: True
pretrained_model: pretrained_model:
checkpoints: checkpoints:
save_inference_dir: save_inference_dir: ./
use_visualdl: False use_visualdl: False
infer_img: doc/imgs_words_en/word_10.png infer_img: doc/imgs_words_en/word_10.png
# for data or label process # for data or label process
...@@ -60,8 +60,8 @@ Metric: ...@@ -60,8 +60,8 @@ Metric:
Train: Train:
dataset: dataset:
name: SimpleDataSet name: SimpleDataSet
data_dir: ./train_data/ data_dir: ./train_data/ic15_data/
label_file_list: ["./train_data/train_list.txt"] label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
transforms: transforms:
- DecodeImage: # load image - DecodeImage: # load image
img_mode: BGR img_mode: BGR
...@@ -81,8 +81,8 @@ Train: ...@@ -81,8 +81,8 @@ Train:
Eval: Eval:
dataset: dataset:
name: SimpleDataSet name: SimpleDataSet
data_dir: ./train_data/ data_dir: ./train_data/ic15_data
label_file_list: ["./train_data/val_list.txt"] label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
transforms: transforms:
- DecodeImage: # load image - DecodeImage: # load image
img_mode: BGR img_mode: BGR
......
deploy/cpp_infer/CMakeLists.txt
View file @ 915d0632
...@@ -37,10 +37,8 @@ endif() ...@@ -37,10 +37,8 @@ endif()
if (WIN32) if (WIN32)
include_directories("${PADDLE_LIB}/paddle/fluid/inference")
include_directories("${PADDLE_LIB}/paddle/include") include_directories("${PADDLE_LIB}/paddle/include")
link_directories("${PADDLE_LIB}/paddle/lib") link_directories("${PADDLE_LIB}/paddle/lib")
link_directories("${PADDLE_LIB}/paddle/fluid/inference")
find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/build/ NO_DEFAULT_PATH) find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/build/ NO_DEFAULT_PATH)
else () else ()
......
deploy/cpp_infer/src/config.cpp
View file @ 915d0632
...@@ -21,12 +21,18 @@ std::vector<std::string> OCRConfig::split(const std::string &str, ...@@ -21,12 +21,18 @@ std::vector<std::string> OCRConfig::split(const std::string &str,
std::vector<std::string> res; std::vector<std::string> res;
if ("" == str) if ("" == str)
return res; return res;
char strs[str.length() + 1];
int strlen = str.length() + 1;
chars *strs = new char[strlen];
std::strcpy(strs, str.c_str()); std::strcpy(strs, str.c_str());
char d[delim.length() + 1]; int delimlen = delim.length() + 1;
char *d = new char[delimlen];
std::strcpy(d, delim.c_str()); std::strcpy(d, delim.c_str());
delete[] strs;
delete[] d;
char *p = std::strtok(strs, d); char *p = std::strtok(strs, d);
while (p) { while (p) {
std::string s = p; std::string s = p;
......
doc/joinus.PNG
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requirements.txt
View file @ 915d0632
...@@ -7,5 +7,5 @@ tqdm ...@@ -7,5 +7,5 @@ tqdm
numpy numpy
visualdl visualdl
python-Levenshtein python-Levenshtein
opencv-contrib-python==4.2.0.32 opencv-contrib-python==4.4.0.46
cython cython
\ No newline at end of file
test/prepare.sh deleted 100644 → 0
View file @ cbc6de9a
#!/bin/bash
FILENAME=$1
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer']
MODE=$2
dataline=$(cat ${FILENAME})
# parser params
IFS=$'\n'
lines=(${dataline})
function func_parser_key(){
strs=$1
IFS=":"
array=(${strs})
tmp=${array[0]}
echo ${tmp}
}
function func_parser_value(){
strs=$1
IFS=":"
array=(${strs})
tmp=${array[1]}
echo ${tmp}
}
IFS=$'\n'
# The training params
model_name=$(func_parser_value "${lines[0]}")
train_model_list=$(func_parser_value "${lines[0]}")
trainer_list=$(func_parser_value "${lines[10]}")
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer']
MODE=$2
# prepare pretrained weights and dataset
if [ ${train_model_list[*]} = "ocr_det" ]; then
wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar
cd pretrain_models && tar xf det_mv3_db_v2.0_train.tar && cd ../
fi
if [ ${MODE} = "lite_train_infer" ];then
# pretrain lite train data
rm -rf ./train_data/icdar2015
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar # todo change to bcebos
cd ./train_data/ && tar xf icdar2015_lite.tar && tar xf ic15_data.tar
ln -s ./icdar2015_lite ./icdar2015
cd ../
epoch=10
eval_batch_step=10
elif [ ${MODE} = "whole_train_infer" ];then
rm -rf ./train_data/icdar2015
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
cd ./train_data/ && tar xf icdar2015.tar && tar xf ic15_data.tar && cd ../
epoch=500
eval_batch_step=200
elif [ ${MODE} = "whole_infer" ];then
rm -rf ./train_data/icdar2015
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_infer.tar
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
cd ./train_data/ && tar xf icdar2015_infer.tar && tar xf ic15_data.tar
ln -s ./icdar2015_infer ./icdar2015
cd ../
epoch=10
eval_batch_step=10
else
rm -rf ./train_data/icdar2015
wget -nc -P ./train_data https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
if [ ${model_name} = "ocr_det" ]; then
eval_model_name="ch_ppocr_mobile_v2.0_det_infer"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
else
eval_model_name="ch_ppocr_mobile_v2.0_rec_train"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
fi
fi
IFS='|'
for train_model in ${train_model_list[*]}; do
if [ ${train_model} = "ocr_det" ];then
model_name="ocr_det"
yml_file="configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
cd ./inference && tar xf ch_det_data_50.tar && cd ../
img_dir="./inference/ch_det_data_50/all-sum-510"
data_dir=./inference/ch_det_data_50/
data_label_file=[./inference/ch_det_data_50/test_gt_50.txt]
elif [ ${train_model} = "ocr_rec" ];then
model_name="ocr_rec"
yml_file="configs/rec/rec_mv3_none_bilstm_ctc.yml"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/rec_inference.tar
cd ./inference && tar xf rec_inference.tar && cd ../
img_dir="./inference/rec_inference/"
data_dir=./inference/rec_inference
data_label_file=[./inference/rec_inference/rec_gt_test.txt]
fi
# eval
for slim_trainer in ${trainer_list[*]}; do
if [ ${slim_trainer} = "norm" ]; then
if [ ${model_name} = "det" ]; then
eval_model_name="ch_ppocr_mobile_v2.0_det_train"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
else
eval_model_name="ch_ppocr_mobile_v2.0_rec_train"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
fi
elif [ ${slim_trainer} = "pact" ]; then
if [ ${model_name} = "det" ]; then
eval_model_name="ch_ppocr_mobile_v2.0_det_quant_train"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_quant_train.tar
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
else
eval_model_name="ch_ppocr_mobile_v2.0_rec_quant_train"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_rec_quant_train.tar
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
fi
elif [ ${slim_trainer} = "distill" ]; then
if [ ${model_name} = "det" ]; then
eval_model_name="ch_ppocr_mobile_v2.0_det_distill_train"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_distill_train.tar
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
else
eval_model_name="ch_ppocr_mobile_v2.0_rec_distill_train"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_rec_distill_train.tar
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
fi
elif [ ${slim_trainer} = "fpgm" ]; then
if [ ${model_name} = "det" ]; then
eval_model_name="ch_ppocr_mobile_v2.0_det_prune_train"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_train.tar
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
else
eval_model_name="ch_ppocr_mobile_v2.0_rec_prune_train"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_rec_prune_train.tar
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
fi
fi
done
done
test/test.sh deleted 100644 → 0
View file @ cbc6de9a
#!/bin/bash
FILENAME=$1
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer']
MODE=$2
dataline=$(cat ${FILENAME})
# parser params
IFS=$'\n'
lines=(${dataline})
function func_parser_key(){
strs=$1
IFS=":"
array=(${strs})
tmp=${array[0]}
echo ${tmp}
}
function func_parser_value(){
strs=$1
IFS=":"
array=(${strs})
tmp=${array[1]}
echo ${tmp}
}
function status_check(){
last_status=$1 # the exit code
run_command=$2
run_log=$3
if [ $last_status -eq 0 ]; then
echo -e "\033[33m Run successfully with command - ${run_command}! \033[0m" | tee -a ${run_log}
else
echo -e "\033[33m Run failed with command - ${run_command}! \033[0m" | tee -a ${run_log}
fi
}
IFS=$'\n'
# The training params
model_name=$(func_parser_value "${lines[0]}")
python=$(func_parser_value "${lines[1]}")
gpu_list=$(func_parser_value "${lines[2]}")
autocast_list=$(func_parser_value "${lines[3]}")
autocast_key=$(func_parser_key "${lines[3]}")
epoch_key=$(func_parser_key "${lines[4]}")
epoch_num=$(func_parser_value "${lines[4]}")
save_model_key=$(func_parser_key "${lines[5]}")
train_batch_key=$(func_parser_key "${lines[6]}")
train_use_gpu_key=$(func_parser_key "${lines[7]}")
pretrain_model_key=$(func_parser_key "${lines[8]}")
pretrain_model_value=$(func_parser_value "${lines[8]}")
trainer_list=$(func_parser_value "${lines[9]}")
norm_trainer=$(func_parser_value "${lines[10]}")
pact_trainer=$(func_parser_value "${lines[11]}")
fpgm_trainer=$(func_parser_value "${lines[12]}")
distill_trainer=$(func_parser_value "${lines[13]}")
eval_py=$(func_parser_value "${lines[14]}")
save_infer_key=$(func_parser_key "${lines[15]}")
export_weight=$(func_parser_key "${lines[16]}")
norm_export=$(func_parser_value "${lines[17]}")
pact_export=$(func_parser_value "${lines[18]}")
fpgm_export=$(func_parser_value "${lines[19]}")
distill_export=$(func_parser_value "${lines[20]}")
inference_py=$(func_parser_value "${lines[21]}")
use_gpu_key=$(func_parser_key "${lines[22]}")
use_gpu_list=$(func_parser_value "${lines[22]}")
use_mkldnn_key=$(func_parser_key "${lines[23]}")
use_mkldnn_list=$(func_parser_value "${lines[23]}")
cpu_threads_key=$(func_parser_key "${lines[24]}")
cpu_threads_list=$(func_parser_value "${lines[24]}")
batch_size_key=$(func_parser_key "${lines[25]}")
batch_size_list=$(func_parser_value "${lines[25]}")
use_trt_key=$(func_parser_key "${lines[26]}")
use_trt_list=$(func_parser_value "${lines[26]}")
precision_key=$(func_parser_key "${lines[27]}")
precision_list=$(func_parser_value "${lines[27]}")
infer_model_key=$(func_parser_key "${lines[28]}")
infer_model=$(func_parser_value "${lines[28]}")
image_dir_key=$(func_parser_key "${lines[29]}")
infer_img_dir=$(func_parser_value "${lines[29]}")
save_log_key=$(func_parser_key "${lines[30]}")
LOG_PATH="./test/output"
mkdir -p ${LOG_PATH}
status_log="${LOG_PATH}/results.log"
function func_inference(){
IFS='|'
_python=$1
_script=$2
_model_dir=$3
_log_path=$4
_img_dir=$5
# inference
for use_gpu in ${use_gpu_list[*]}; do
if [ ${use_gpu} = "False" ]; then
for use_mkldnn in ${use_mkldnn_list[*]}; do
for threads in ${cpu_threads_list[*]}; do
for batch_size in ${batch_size_list[*]}; do
_save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log"
command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${cpu_threads_key}=${threads} ${infer_model_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir} ${save_log_key}=${_save_log_path} --benchmark=True"
eval $command
status_check $? "${command}" "${status_log}"
done
done
done
else
for use_trt in ${use_trt_list[*]}; do
for precision in ${precision_list[*]}; do
if [ ${use_trt} = "False" ] && [ ${precision} != "fp32" ]; then
continue
fi
for batch_size in ${batch_size_list[*]}; do
_save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_trt_key}=${use_trt} ${precision_key}=${precision} ${infer_model_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir} ${save_log_key}=${_save_log_path} --benchmark=True"
eval $command
status_check $? "${command}" "${status_log}"
done
done
done
fi
done
}
if [ ${MODE} != "infer" ]; then
IFS="|"
for gpu in ${gpu_list[*]}; do
use_gpu=True
if [ ${gpu} = "-1" ];then
use_gpu=False
env=""
elif [ ${#gpu} -le 1 ];then
env="export CUDA_VISIBLE_DEVICES=${gpu}"
eval ${env}
elif [ ${#gpu} -le 15 ];then
IFS=","
array=(${gpu})
env="export CUDA_VISIBLE_DEVICES=${array[0]}"
IFS="|"
else
IFS=";"
array=(${gpu})
ips=${array[0]}
gpu=${array[1]}
IFS="|"
env=" "
fi
for autocast in ${autocast_list[*]}; do
for trainer in ${trainer_list[*]}; do
if [ ${trainer} = "pact" ]; then
run_train=${pact_trainer}
run_export=${pact_export}
elif [ ${trainer} = "fpgm" ]; then
run_train=${fpgm_trainer}
run_export=${fpgm_export}
elif [ ${trainer} = "distill" ]; then
run_train=${distill_trainer}
run_export=${distill_export}
else
run_train=${norm_trainer}
run_export=${norm_export}
fi
if [ ${run_train} = "null" ]; then
continue
fi
if [ ${run_export} = "null" ]; then
continue
fi
# not set autocast when autocast is null
if [ ${autocast} = "null" ]; then
set_autocast=" "
else
set_autocast="${autocast_key}=${autocast}"
fi
# not set epoch when whole_train_infer
if [ ${MODE} != "whole_train_infer" ]; then
set_epoch="${epoch_key}=${epoch_num}"
else
set_epoch=" "
fi
# set pretrain
if [ ${pretrain_model_value} != "null" ]; then
set_pretrain="${pretrain_model_key}=${pretrain_model_value}"
else
set_pretrain=" "
fi
save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
if [ ${#gpu} -le 2 ];then # train with cpu or single gpu
cmd="${python} ${run_train} ${train_use_gpu_key}=${use_gpu} ${save_model_key}=${save_log} ${set_epoch} ${set_pretrain} ${set_autocast}"
elif [ ${#gpu} -le 15 ];then # train with multi-gpu
cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${save_model_key}=${save_log} ${set_epoch} ${set_pretrain} ${set_autocast}"
else # train with multi-machine
cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${save_model_key}=${save_log} ${set_pretrain} ${set_epoch} ${set_autocast}"
fi
# run train
eval $cmd
status_check $? "${cmd}" "${status_log}"
# run eval
eval_cmd="${python} ${eval_py} ${save_model_key}=${save_log} ${pretrain_model_key}=${save_log}/latest"
eval $eval_cmd
status_check $? "${eval_cmd}" "${status_log}"
# run export model
save_infer_path="${save_log}"
export_cmd="${python} ${run_export} ${save_model_key}=${save_log} ${export_weight}=${save_log}/latest ${save_infer_key}=${save_infer_path}"
eval $export_cmd
status_check $? "${export_cmd}" "${status_log}"
#run inference
eval $env
save_infer_path="${save_log}"
func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${infer_img_dir}"
eval "unset CUDA_VISIBLE_DEVICES"
done
done
done
else
GPUID=$3
if [ ${#GPUID} -le 0 ];then
env=" "
else
env="export CUDA_VISIBLE_DEVICES=${GPUID}"
fi
echo $env
#run inference
func_inference "${python}" "${inference_py}" "${infer_model}" "${LOG_PATH}" "${infer_img_dir}"
fi
test/ocr_det_params.txt → tests/ocr_det_params.txt
View file @ 915d0632
===========================train_params===========================
model_name:ocr_det model_name:ocr_det
python:python3.7 python:python3.7
gpu_list:0|0,1 gpu_list:0|0,1
Global.use_gpu:True|True
Global.auto_cast:null Global.auto_cast:null
Global.epoch_num:10 Global.epoch_num:lite_train_infer=2|whole_train_infer=300
Global.save_model_dir:./output/ Global.save_model_dir:./output/
Train.loader.batch_size_per_card: Train.loader.batch_size_per_card:lite_train_infer=2|whole_train_infer=4
Global.use_gpu:
Global.pretrained_model:null Global.pretrained_model:null
train_model_name:latest
trainer:norm|pact train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
null:null
##
trainer:norm_train|pact_train
norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
quant_train:deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy pact_train:deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o
fpgm_train:null fpgm_train:null
distill_train:null distill_train:null
null:null
eval:tools/eval.py -c configs/det/det_mv3_db.yml -o null:null
##
===========================eval_params===========================
eval:tools/eval.py -c configs/det/det_mv3_db.yml -o
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/ Global.save_inference_dir:./output/
Global.pretrained_model: Global.pretrained_model:
norm_export:tools/export_model.py -c configs/det/det_mv3_db.yml -o norm_export:tools/export_model.py -c configs/det/det_mv3_db.yml -o
quant_export:deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o quant_export:deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o
fpgm_export:deploy/slim/prune/export_prune_model.py fpgm_export:deploy/slim/prune/export_prune_model.py
distill_export:null distill_export:null
export1:null
export2:null
##
infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
infer_export:null
infer_quant:False
inference:tools/infer/predict_det.py inference:tools/infer/predict_det.py
--use_gpu:True|False --use_gpu:True|False
--enable_mkldnn:True|False --enable_mkldnn:True|False
--cpu_threads:1|6 --cpu_threads:1|6
--rec_batch_num:1 --rec_batch_num:1
--use_tensorrt:True|False --use_tensorrt:False|True
--precision:fp32|fp16|int8 --precision:fp32|fp16|int8
--det_model_dir:./inference/ch_ppocr_mobile_v2.0_det_infer/ --det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/ --image_dir:./inference/ch_det_data_50/all-sum-510/
--save_log_path:./test/output/ --save_log_path:null
--benchmark:True
null:null
test/ocr_rec_params.txt → tests/ocr_rec_params.txt
View file @ 915d0632
===========================train_params===========================
model_name:ocr_rec model_name:ocr_rec
python:python python:python3.7
gpu_list:0|0,1 gpu_list:0|2,3
Global.use_gpu:True|True
Global.auto_cast:null Global.auto_cast:null
Global.epoch_num:10 Global.epoch_num:lite_train_infer=2|whole_train_infer=300
Global.save_model_dir:./output/ Global.save_model_dir:./output/
Train.loader.batch_size_per_card: Train.loader.batch_size_per_card:lite_train_infer=128|whole_train_infer=128
Global.use_gpu:
Global.pretrained_model:null Global.pretrained_model:null
train_model_name:latest
trainer:norm|pact train_infer_img_dir:./train_data/ic15_data/train
norm_train:tools/train.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml null:null
quant_train:deploy/slim/quantization/quant.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml ##
trainer:norm_train|pact_train
norm_train:tools/train.py -c configs/rec/rec_icdar15_train.yml -o
pact_train:deploy/slim/quantization/quant.py -c configs/rec/rec_icdar15_train.yml -o
fpgm_train:null fpgm_train:null
distill_train:null distill_train:null
null:null
eval:tools/eval.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml -o null:null
##
===========================eval_params===========================
eval:tools/eval.py -c configs/rec/rec_icdar15_train.yml -o
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/ Global.save_inference_dir:./output/
Global.pretrained_model: Global.pretrained_model:
norm_export:tools/export_model.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml -o norm_export:tools/export_model.py -c configs/rec/rec_icdar15_train.yml -o
quant_export:deploy/slim/quantization/export_model.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml -o quant_export:deploy/slim/quantization/export_model.py -c configs/rec/rec_icdar15_train.yml -o
fpgm_export:null fpgm_export:null
distill_export:null distill_export:null
export1:null
export2:null
##
infer_model:./inference/ch_ppocr_mobile_v2.0_rec_infer/
infer_export:null
infer_quant:False
inference:tools/infer/predict_rec.py inference:tools/infer/predict_rec.py
--use_gpu:True|False --use_gpu:True|False
--enable_mkldnn:True|False --enable_mkldnn:True|False
...@@ -30,6 +44,8 @@ inference:tools/infer/predict_rec.py ...@@ -30,6 +44,8 @@ inference:tools/infer/predict_rec.py
--rec_batch_num:1 --rec_batch_num:1
--use_tensorrt:True|False --use_tensorrt:True|False
--precision:fp32|fp16|int8 --precision:fp32|fp16|int8
--rec_model_dir:./inference/ch_ppocr_mobile_v2.0_rec_infer/ --rec_model_dir:
--image_dir:./inference/rec_inference --image_dir:./inference/rec_inference
--save_log_path:./test/output/ --save_log_path:./test/output/
\ No newline at end of file --benchmark:True
null:null
tests/prepare.sh 0 → 100644
View file @ 915d0632
#!/bin/bash
FILENAME=$1
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer']
MODE=$2
dataline=$(cat ${FILENAME})
# parser params
IFS=$'\n'
lines=(${dataline})
function func_parser_key(){
strs=$1
IFS=":"
array=(${strs})
tmp=${array[0]}
echo ${tmp}
}
function func_parser_value(){
strs=$1
IFS=":"
array=(${strs})
tmp=${array[1]}
echo ${tmp}
}
IFS=$'\n'
# The training params
model_name=$(func_parser_value "${lines[1]}")
trainer_list=$(func_parser_value "${lines[14]}")
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer']
MODE=$2
if [ ${MODE} = "lite_train_infer" ];then
# pretrain lite train data
wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
rm -rf ./train_data/icdar2015
rm -rf ./train_data/ic15_data
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar # todo change to bcebos
cd ./train_data/ && tar xf icdar2015_lite.tar && tar xf ic15_data.tar
ln -s ./icdar2015_lite ./icdar2015
cd ../
elif [ ${MODE} = "whole_train_infer" ];then
wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
rm -rf ./train_data/icdar2015
rm -rf ./train_data/ic15_data
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
cd ./train_data/ && tar xf icdar2015.tar && tar xf ic15_data.tar && cd ../
elif [ ${MODE} = "whole_infer" ];then
wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
rm -rf ./train_data/icdar2015
rm -rf ./train_data/ic15_data
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_infer.tar
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
cd ./train_data/ && tar xf icdar2015_infer.tar && tar xf ic15_data.tar
ln -s ./icdar2015_infer ./icdar2015
cd ../
else
if [ ${model_name} = "ocr_det" ]; then
eval_model_name="ch_ppocr_mobile_v2.0_det_infer"
rm -rf ./train_data/icdar2015
wget -nc -P ./train_data https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && cd ../
else
rm -rf ./train_data/ic15_data
eval_model_name="ch_ppocr_mobile_v2.0_rec_infer"
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
cd ./inference && tar xf ${eval_model_name}.tar && tar xf ic15_data.tar && cd ../
fi
fi
tests/test.sh 0 → 100644
View file @ 915d0632
#!/bin/bash
FILENAME=$1
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer']
MODE=$2
dataline=$(cat ${FILENAME})
# parser params
IFS=$'\n'
lines=(${dataline})
function func_parser_key(){
strs=$1
IFS=":"
array=(${strs})
tmp=${array[0]}
echo ${tmp}
}
function func_parser_value(){
strs=$1
IFS=":"
array=(${strs})
tmp=${array[1]}
echo ${tmp}
}
function func_set_params(){
key=$1
value=$2
if [ ${key} = "null" ];then
echo " "
elif [[ ${value} = "null" ]] || [[ ${value} = " " ]] || [ ${#value} -le 0 ];then
echo " "
else
echo "${key}=${value}"
fi
}
function func_parser_params(){
strs=$1
IFS=":"
array=(${strs})
key=${array[0]}
tmp=${array[1]}
IFS="|"
res=""
for _params in ${tmp[*]}; do
IFS="="
array=(${_params})
mode=${array[0]}
value=${array[1]}
if [[ ${mode} = ${MODE} ]]; then
IFS="|"
#echo $(func_set_params "${mode}" "${value}")
echo $value
break
fi
IFS="|"
done
echo ${res}
}
function status_check(){
last_status=$1 # the exit code
run_command=$2
run_log=$3
if [ $last_status -eq 0 ]; then
echo -e "\033[33m Run successfully with command - ${run_command}! \033[0m" | tee -a ${run_log}
else
echo -e "\033[33m Run failed with command - ${run_command}! \033[0m" | tee -a ${run_log}
fi
}
IFS=$'\n'
# The training params
model_name=$(func_parser_value "${lines[1]}")
python=$(func_parser_value "${lines[2]}")
gpu_list=$(func_parser_value "${lines[3]}")
train_use_gpu_key=$(func_parser_key "${lines[4]}")
train_use_gpu_value=$(func_parser_value "${lines[4]}")
autocast_list=$(func_parser_value "${lines[5]}")
autocast_key=$(func_parser_key "${lines[5]}")
epoch_key=$(func_parser_key "${lines[6]}")
epoch_num=$(func_parser_params "${lines[6]}")
save_model_key=$(func_parser_key "${lines[7]}")
train_batch_key=$(func_parser_key "${lines[8]}")
train_batch_value=$(func_parser_params "${lines[8]}")
pretrain_model_key=$(func_parser_key "${lines[9]}")
pretrain_model_value=$(func_parser_value "${lines[9]}")
train_model_name=$(func_parser_value "${lines[10]}")
train_infer_img_dir=$(func_parser_value "${lines[11]}")
train_param_key1=$(func_parser_key "${lines[12]}")
train_param_value1=$(func_parser_value "${lines[12]}")
trainer_list=$(func_parser_value "${lines[14]}")
trainer_norm=$(func_parser_key "${lines[15]}")
norm_trainer=$(func_parser_value "${lines[15]}")
pact_key=$(func_parser_key "${lines[16]}")
pact_trainer=$(func_parser_value "${lines[16]}")
fpgm_key=$(func_parser_key "${lines[17]}")
fpgm_trainer=$(func_parser_value "${lines[17]}")
distill_key=$(func_parser_key "${lines[18]}")
distill_trainer=$(func_parser_value "${lines[18]}")
trainer_key1=$(func_parser_key "${lines[19]}")
trainer_value1=$(func_parser_value "${lines[19]}")
trainer_key2=$(func_parser_key "${lines[20]}")
trainer_value2=$(func_parser_value "${lines[20]}")
eval_py=$(func_parser_value "${lines[23]}")
eval_key1=$(func_parser_key "${lines[24]}")
eval_value1=$(func_parser_value "${lines[24]}")
save_infer_key=$(func_parser_key "${lines[27]}")
export_weight=$(func_parser_key "${lines[28]}")
norm_export=$(func_parser_value "${lines[29]}")
pact_export=$(func_parser_value "${lines[30]}")
fpgm_export=$(func_parser_value "${lines[31]}")
distill_export=$(func_parser_value "${lines[32]}")
export_key1=$(func_parser_key "${lines[33]}")
export_value1=$(func_parser_value "${lines[33]}")
export_key2=$(func_parser_key "${lines[34]}")
export_value2=$(func_parser_value "${lines[34]}")
# parser inference model
infer_model_dir_list=$(func_parser_value "${lines[36]}")
infer_export_list=$(func_parser_value "${lines[37]}")
infer_is_quant=$(func_parser_value "${lines[38]}")
# parser inference
inference_py=$(func_parser_value "${lines[39]}")
use_gpu_key=$(func_parser_key "${lines[40]}")
use_gpu_list=$(func_parser_value "${lines[40]}")
use_mkldnn_key=$(func_parser_key "${lines[41]}")
use_mkldnn_list=$(func_parser_value "${lines[41]}")
cpu_threads_key=$(func_parser_key "${lines[42]}")
cpu_threads_list=$(func_parser_value "${lines[42]}")
batch_size_key=$(func_parser_key "${lines[43]}")
batch_size_list=$(func_parser_value "${lines[43]}")
use_trt_key=$(func_parser_key "${lines[44]}")
use_trt_list=$(func_parser_value "${lines[44]}")
precision_key=$(func_parser_key "${lines[45]}")
precision_list=$(func_parser_value "${lines[45]}")
infer_model_key=$(func_parser_key "${lines[46]}")
image_dir_key=$(func_parser_key "${lines[47]}")
infer_img_dir=$(func_parser_value "${lines[47]}")
save_log_key=$(func_parser_key "${lines[48]}")
benchmark_key=$(func_parser_key "${lines[49]}")
benchmark_value=$(func_parser_value "${lines[49]}")
infer_key1=$(func_parser_key "${lines[50]}")
infer_value1=$(func_parser_value "${lines[50]}")
LOG_PATH="./tests/output"
mkdir -p ${LOG_PATH}
status_log="${LOG_PATH}/results.log"
function func_inference(){
IFS='|'
_python=$1
_script=$2
_model_dir=$3
_log_path=$4
_img_dir=$5
_flag_quant=$6
# inference
for use_gpu in ${use_gpu_list[*]}; do
if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
for use_mkldnn in ${use_mkldnn_list[*]}; do
if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
continue
fi
for threads in ${cpu_threads_list[*]}; do
for batch_size in ${batch_size_list[*]}; do
_save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log"
set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}")
set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
eval $command
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${command}" "${status_log}"
done
done
done
elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
for use_trt in ${use_trt_list[*]}; do
for precision in ${precision_list[*]}; do
if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
continue
fi
if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
continue
fi
if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then
continue
fi
for batch_size in ${batch_size_list[*]}; do
_save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
set_tensorrt=$(func_set_params "${use_trt_key}" "${use_trt}")
set_precision=$(func_set_params "${precision_key}" "${precision}")
set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
eval $command
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${command}" "${status_log}"
done
done
done
else
echo "Does not support hardware other than CPU and GPU Currently!"
fi
done
}
if [ ${MODE} = "infer" ]; then
GPUID=$3
if [ ${#GPUID} -le 0 ];then
env=" "
else
env="export CUDA_VISIBLE_DEVICES=${GPUID}"
fi
# set CUDA_VISIBLE_DEVICES
eval $env
export Count=0
IFS="|"
infer_run_exports=(${infer_export_list})
infer_quant_flag=(${infer_is_quant})
for infer_model in ${infer_model_dir_list[*]}; do
# run export
if [ ${infer_run_exports[Count]} != "null" ];then
save_infer_dir=$(dirname $infer_model)
set_export_weight=$(func_set_params "${export_weight}" "${infer_model}")
set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_dir}")
export_cmd="${python} ${norm_export} ${set_export_weight} ${set_save_infer_key}"
eval $export_cmd
status_export=$?
if [ ${status_export} = 0 ];then
status_check $status_export "${export_cmd}" "${status_log}"
fi
else
save_infer_dir=${infer_model}
fi
#run inference
is_quant=${infer_quant_flag[Count]}
func_inference "${python}" "${inference_py}" "${save_infer_dir}" "${LOG_PATH}" "${infer_img_dir}" ${is_quant}
Count=$(($Count + 1))
done
else
IFS="|"
export Count=0
USE_GPU_KEY=(${train_use_gpu_value})
for gpu in ${gpu_list[*]}; do
use_gpu=${USE_GPU_KEY[Count]}
Count=$(($Count + 1))
if [ ${gpu} = "-1" ];then
env=""
elif [ ${#gpu} -le 1 ];then
env="export CUDA_VISIBLE_DEVICES=${gpu}"
eval ${env}
elif [ ${#gpu} -le 15 ];then
IFS=","
array=(${gpu})
env="export CUDA_VISIBLE_DEVICES=${array[0]}"
IFS="|"
else
IFS=";"
array=(${gpu})
ips=${array[0]}
gpu=${array[1]}
IFS="|"
env=" "
fi
for autocast in ${autocast_list[*]}; do
for trainer in ${trainer_list[*]}; do
flag_quant=False
if [ ${trainer} = ${pact_key} ]; then
run_train=${pact_trainer}
run_export=${pact_export}
flag_quant=True
elif [ ${trainer} = "${fpgm_key}" ]; then
run_train=${fpgm_trainer}
run_export=${fpgm_export}
elif [ ${trainer} = "${distill_key}" ]; then
run_train=${distill_trainer}
run_export=${distill_export}
elif [ ${trainer} = ${trainer_key1} ]; then
run_train=${trainer_value1}
run_export=${export_value1}
elif [[ ${trainer} = ${trainer_key2} ]]; then
run_train=${trainer_value2}
run_export=${export_value2}
else
run_train=${norm_trainer}
run_export=${norm_export}
fi
if [ ${run_train} = "null" ]; then
continue
fi
set_autocast=$(func_set_params "${autocast_key}" "${autocast}")
set_epoch=$(func_set_params "${epoch_key}" "${epoch_num}")
set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}")
set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}")
set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}")
set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${use_gpu}")
save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
# load pretrain from norm training if current trainer is pact or fpgm trainer
if [ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]; then
set_pretrain="${load_norm_train_model}"
fi
set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
if [ ${#gpu} -le 2 ];then # train with cpu or single gpu
cmd="${python} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} "
elif [ ${#gpu} -le 15 ];then # train with multi-gpu
cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1}"
else # train with multi-machine
cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1}"
fi
# run train
eval "unset CUDA_VISIBLE_DEVICES"
eval $cmd
status_check $? "${cmd}" "${status_log}"
set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}")
# save norm trained models to set pretrain for pact training and fpgm training
if [ ${trainer} = ${trainer_norm} ]; then
load_norm_train_model=${set_eval_pretrain}
fi
# run eval
if [ ${eval_py} != "null" ]; then
set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}")
eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu} ${set_eval_params1}"
eval $eval_cmd
status_check $? "${eval_cmd}" "${status_log}"
fi
# run export model
if [ ${run_export} != "null" ]; then
# run export model
save_infer_path="${save_log}"
set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${train_model_name}")
set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_path}")
export_cmd="${python} ${run_export} ${set_export_weight} ${set_save_infer_key}"
eval $export_cmd
status_check $? "${export_cmd}" "${status_log}"
#run inference
eval $env
save_infer_path="${save_log}"
func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}"
eval "unset CUDA_VISIBLE_DEVICES"
fi
done # done with: for trainer in ${trainer_list[*]}; do
done # done with: for autocast in ${autocast_list[*]}; do
done # done with: for gpu in ${gpu_list[*]}; do
fi # end if [ ${MODE} = "infer" ]; then
tools/infer/predict_det.py
View file @ 915d0632
...@@ -114,7 +114,7 @@ class TextDetector(object): ...@@ -114,7 +114,7 @@ class TextDetector(object):
model_precision=args.precision, model_precision=args.precision,
batch_size=1, batch_size=1,
data_shape="dynamic", data_shape="dynamic",
save_path=args.save_log_path, save_path=None,
inference_config=self.config, inference_config=self.config,
pids=pid, pids=pid,
process_name=None, process_name=None,
...@@ -122,7 +122,8 @@ class TextDetector(object): ...@@ -122,7 +122,8 @@ class TextDetector(object):
time_keys=[ time_keys=[
'preprocess_time', 'inference_time', 'postprocess_time' 'preprocess_time', 'inference_time', 'postprocess_time'
], ],
warmup=10) warmup=2,
logger=logger)
def order_points_clockwise(self, pts): def order_points_clockwise(self, pts):
""" """
...@@ -244,7 +245,7 @@ if __name__ == "__main__": ...@@ -244,7 +245,7 @@ if __name__ == "__main__":
if args.warmup: if args.warmup:
img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8) img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8)
for i in range(10): for i in range(2):
res = text_detector(img) res = text_detector(img)
if not os.path.exists(draw_img_save): if not os.path.exists(draw_img_save):
......
tools/infer/predict_rec.py
View file @ 915d0632
...@@ -73,7 +73,7 @@ class TextRecognizer(object): ...@@ -73,7 +73,7 @@ class TextRecognizer(object):
model_precision=args.precision, model_precision=args.precision,
batch_size=args.rec_batch_num, batch_size=args.rec_batch_num,
data_shape="dynamic", data_shape="dynamic",
save_path=args.save_log_path, save_path=None, #args.save_log_path,
inference_config=self.config, inference_config=self.config,
pids=pid, pids=pid,
process_name=None, process_name=None,
...@@ -81,7 +81,8 @@ class TextRecognizer(object): ...@@ -81,7 +81,8 @@ class TextRecognizer(object):
time_keys=[ time_keys=[
'preprocess_time', 'inference_time', 'postprocess_time' 'preprocess_time', 'inference_time', 'postprocess_time'
], ],
warmup=10) warmup=2,
logger=logger)
def resize_norm_img(self, img, max_wh_ratio): def resize_norm_img(self, img, max_wh_ratio):
imgC, imgH, imgW = self.rec_image_shape imgC, imgH, imgW = self.rec_image_shape
...@@ -272,10 +273,10 @@ def main(args): ...@@ -272,10 +273,10 @@ def main(args):
valid_image_file_list = [] valid_image_file_list = []
img_list = [] img_list = []
# warmup 10 times # warmup 2 times
if args.warmup: if args.warmup:
img = np.random.uniform(0, 255, [32, 320, 3]).astype(np.uint8) img = np.random.uniform(0, 255, [32, 320, 3]).astype(np.uint8)
for i in range(10): for i in range(2):
res = text_recognizer([img]) res = text_recognizer([img])
for image_file in image_file_list: for image_file in image_file_list:
......
tools/infer/utility.py
View file @ 915d0632
...@@ -216,6 +216,27 @@ def create_predictor(args, mode, logger): ...@@ -216,6 +216,27 @@ def create_predictor(args, mode, logger):
"elementwise_add_7": [1, 56, 40, 40], "elementwise_add_7": [1, 56, 40, 40],
"nearest_interp_v2_0.tmp_0": [1, 256, 40, 40] "nearest_interp_v2_0.tmp_0": [1, 256, 40, 40]
} }
min_pact_shape = {
"nearest_interp_v2_26.tmp_0":[1,256,20,20],
"nearest_interp_v2_27.tmp_0":[1,64,20,20],
"nearest_interp_v2_28.tmp_0":[1,64,20,20],
"nearest_interp_v2_29.tmp_0":[1,64,20,20]
}
max_pact_shape = {
"nearest_interp_v2_26.tmp_0":[1,256,400,400],
"nearest_interp_v2_27.tmp_0":[1,64,400,400],
"nearest_interp_v2_28.tmp_0":[1,64,400,400],
"nearest_interp_v2_29.tmp_0":[1,64,400,400]
}
opt_pact_shape = {
"nearest_interp_v2_26.tmp_0":[1,256,160,160],
"nearest_interp_v2_27.tmp_0":[1,64,160,160],
"nearest_interp_v2_28.tmp_0":[1,64,160,160],
"nearest_interp_v2_29.tmp_0":[1,64,160,160]
}
min_input_shape.update(min_pact_shape)
max_input_shape.update(max_pact_shape)
opt_input_shape.update(opt_pact_shape)
elif mode == "rec": elif mode == "rec":
min_input_shape = {"x": [args.rec_batch_num, 3, 32, 10]} min_input_shape = {"x": [args.rec_batch_num, 3, 32, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 32, 2000]} max_input_shape = {"x": [args.rec_batch_num, 3, 32, 2000]}
......
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