提交 069d994c 编写于 作者: L LDOUBLEV

test to test_v5

上级 3ba4d543
model_name:ocr_det
python:python3.7
gpu_list:-1|0|0,1
Global.auto_cast:False|True
Global.epoch_num:10
Global.save_model_dir:./output/
Global.save_inference_dir:./output/
Train.loader.batch_size_per_card:
Global.use_gpu
Global.pretrained_model
trainer:norm|pact|fpgm
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
fpgm_train:null
distill_train:null
eval:tools/eval.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
fpgm_export:deploy/slim/prune/export_prune_model.py
distill_export:null
inference:tools/infer/predict_det.py
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:True|False
--precision:fp32|fp16|int8
--det_model_dir
--image_dir
--save_log_path
train_model_list: ocr_det
gpu_list: -1|0|0,1
auto_cast_list: False|True
trainer_list: norm|pact|fpgm
python: python3.7
inference: python
devices: cpu|gpu
use_mkldnn_list: True|False
cpu_threads_list: 1|6
rec_batch_size_list: 1|6
gpu_trt_list: True|False
gpu_precision_list: fp32|fp16|int8
infer_gpu_id: 0
log_path: ./output
#!/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(){
function func_parser_key(){
strs=$1
IFS=":"
array=(${strs})
tmp=${array[0]}
echo ${tmp}
}
function func_parser_value(){
strs=$1
IFS=": "
IFS=":"
array=(${strs})
tmp=${array[1]}
echo ${tmp}
}
IFS=$'\n'
# The training params
train_model_list=$(func_parser "${lines[0]}")
slim_trainer_list=$(func_parser "${lines[3]}")
python=$(func_parser "${lines[4]}")
# inference params
# inference=$(func_parser "${lines[5]}")
devices=$(func_parser "${lines[6]}")
use_mkldnn_list=$(func_parser "${lines[7]}")
cpu_threads_list=$(func_parser "${lines[8]}")
rec_batch_size_list=$(func_parser "${lines[9]}")
gpu_trt_list=$(func_parser "${lines[10]}")
gpu_precision_list=$(func_parser "${lines[11]}")
model_name=$(func_parser_value "${lines[0]}")
train_model_list=$(func_parser_value "${lines[0]}")
slim_trainer_list=$(func_parser_value "${lines[12]}")
infer_gpu_id=$(func_parser "${lines[12]}")
log_path=$(func_parser "${lines[13]}")
status_log="${log_path}/result.log"
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer']
MODE=$2
# prepare pretrained weights and dataset
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 ../
# install requirments
${python} -m pip install pynvml;
${python} -m pip install psutil;
${python} -m pip install GPUtil;
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
cd ./train_data/ && tar xf icdar2015_lite.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
cd ./train_data/ && tar xf icdar2015.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
cd ./train_data/ && tar xf icdar2015_infer.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_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 ${eval_model_name}.tar && cd ../
fi
fi
paddle_info="$(${python} -c "import paddle;print(f'paddle_version:{paddle.__version__}');print(f'paddle_commit:{paddle.__git_commit__}')")"
echo -e "\033[33m $paddle_info \033[0m" | tee -a ${status_log}
cpu_model=`cat /proc/cpuinfo | grep "model name" | awk -F ':' '{print $2}' | sort | uniq`
echo -e "\033[33m cpu_info:$cpu_model \033[0m" | tee -a ${status_log}
ip=`ifconfig| grep -A 1 'eth0'|grep 'inet'|awk -F ':' '{print $2}'|awk '{print $1}'`
echo -e "\033[33m ip_info:$ip \033[0m" | tee -a ${status_log}
function status_check(){
last_status=$1 # the exit code
run_model=$2
run_command=$3
run_log=$4
if [ $last_status -eq 0 ]; then
echo -e "\033[33m $run_model successfully with command - ${run_command}! \033[0m" | tee -a ${run_log}
else
echo -e "\033[33m $case failed with command - ${run_command}! \033[0m" | tee -a ${run_log}
fi
}
IFS='|'
for train_model in ${train_model_list[*]}; do
if [ ${train_model} = "ocr_det" ];then
......@@ -113,61 +134,5 @@ for train_model in ${train_model_list[*]}; do
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
fi
fi
save_log_path="${log_path}/${eval_model_name}"
command="${python} tools/eval.py -c ${yml_file} -o Global.pretrained_model='./inference/${eval_model_name}/best_accuracy' Global.save_model_dir=${save_log_path} Eval.dataset.data_dir=${data_dir} Eval.dataset.label_file_list=${data_label_file}"
${python} tools/eval.py -c ${yml_file} -o Global.pretrained_model=./inference/${eval_model_name}/best_accuracy Global.save_model_dir=${save_log_path} Eval.dataset.data_dir=${data_dir} Eval.dataset.label_file_list=${data_label_file}
status_check $? "${trainer}" "${command}" "${status_log}"
command="${python} tools/export_model.py -c ${yml_file} -o Global.pretrained_model="${eval_model_name}/best_accuracy" Global.save_inference_dir=${log_path}/${eval_model_name}_infer Global.save_model_dir=${save_log_path}"
${python} tools/export_model.py -c ${yml_file} -o Global.pretrained_model="./inference/${eval_model_name}/best_accuracy" Global.save_inference_dir="${log_path}/${eval_model_name}_infer" Global.save_model_dir=${save_log_path}
status_check $? "${trainer}" "${command}" "${status_log}"
if [ $? -eq 0 ]; then
echo -e "\033[33m training of $model_name successfully!\033[0m" | tee -a ${save_log}/train.log
else
cat ${save_log}/train.log
echo -e "\033[33m training of $model_name failed!\033[0m" | tee -a ${save_log}/train.log
fi
if [ "${model_name}" = "det" ]; then
export rec_batch_size_list=( "1" )
inference="tools/infer/predict_det.py"
det_model_dir="${log_path}/${eval_model_name}_infer"
rec_model_dir=""
elif [ "${model_name}" = "rec" ]; then
inference="tools/infer/predict_rec.py"
rec_model_dir="${log_path}/${eval_model_name}_infer"
det_model_dir=""
fi
# inference
for device in ${devices[*]}; do
if [ ${device} = "cpu" ]; then
for use_mkldnn in ${use_mkldnn_list[*]}; do
for threads in ${cpu_threads_list[*]}; do
for rec_batch_size in ${rec_batch_size_list[*]}; do
save_log_path="${log_path}/${model_name}_${slim_trainer}_cpu_usemkldnn_${use_mkldnn}_cputhreads_${threads}_recbatchnum_${rec_batch_size}_infer.log"
command="${python} ${inference} --enable_mkldnn=${use_mkldnn} --use_gpu=False --cpu_threads=${threads} --benchmark=True --det_model_dir=${det_model_dir} --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir} --image_dir=${img_dir} --save_log_path=${save_log_path}"
${python} ${inference} --enable_mkldnn=${use_mkldnn} --use_gpu=False --cpu_threads=${threads} --benchmark=True --det_model_dir=${det_model_dir} --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir} --image_dir=${img_dir} --save_log_path=${save_log_path}
status_check $? "${trainer}" "${command}" "${status_log}"
done
done
done
else
# env="export CUDA_VISIBLE_DEVICES=${infer_gpu_id}"
for use_trt in ${gpu_trt_list[*]}; do
for precision in ${gpu_precision_list[*]}; do
if [ ${use_trt} = "False" ] && [ ${precision} != "fp32" ]; then
continue
fi
for rec_batch_size in ${rec_batch_size_list[*]}; do
save_log_path="${log_path}/${model_name}_${slim_trainer}_gpu_usetensorrt_${use_trt}_usefp16_${precision}_recbatchnum_${rec_batch_size}_infer.log"
command="${python} ${inference} --use_gpu=True --use_tensorrt=${use_trt} --precision=${precision} --benchmark=True --det_model_dir=${log_path}/${eval_model_name}_infer --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir} --image_dir=${img_dir} --save_log_path=${save_log_path}"
${python} ${inference} --use_gpu=True --use_tensorrt=${use_trt} --precision=${precision} --benchmark=True --det_model_dir=${log_path}/${eval_model_name}_infer --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir} --image_dir=${img_dir} --save_log_path=${save_log_path}
status_check $? "${trainer}" "${command}" "${status_log}"
done
done
done
fi
done
done
done
#!/bin/bash
# Usage:
# bash test/test.sh ./test/paddleocr_ci_params.txt 'lite_train_infer'
#!/bin/bash
FILENAME=$1
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer']
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer']
MODE=$2
# prepare pretrained weights and dataset
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 ../
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
cd ./train_data/ && tar xf icdar2015_lite.tar
ln -s ./icdar2015_lite ./icdar2015
cd ../
epoch=10
eval_batch_step=10
elif [ ${MODE} = "whole_train_infer" ];then
rm -rf ./train_data/icdar2015
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar
cd ./train_data/ && tar xf icdar2015.tar && cd ../
epoch=500
eval_batch_step=200
else
rm -rf ./train_data/icdar2015
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_infer.tar
cd ./train_data/ && tar xf icdar2015_infer.tar
ln -s ./icdar2015_infer ./icdar2015
cd ../
epoch=10
eval_batch_step=10
fi
img_dir="./train_data/icdar2015/text_localization/ch4_test_images/"
dataline=$(cat ${FILENAME})
# parser params
IFS=$'\n'
lines=(${dataline})
function func_parser(){
function func_parser_key(){
strs=$1
IFS=":"
array=(${strs})
tmp=${array[0]}
echo ${tmp}
}
function func_parser_value(){
strs=$1
IFS=": "
IFS=":"
array=(${strs})
tmp=${array[1]}
echo ${tmp}
}
IFS=$'\n'
# The training params
train_model_list=$(func_parser "${lines[0]}")
gpu_list=$(func_parser "${lines[1]}")
auto_cast_list=$(func_parser "${lines[2]}")
slim_trainer_list=$(func_parser "${lines[3]}")
python=$(func_parser "${lines[4]}")
# inference params
inference=$(func_parser "${lines[5]}")
devices=$(func_parser "${lines[6]}")
use_mkldnn_list=$(func_parser "${lines[7]}")
cpu_threads_list=$(func_parser "${lines[8]}")
rec_batch_size_list=$(func_parser "${lines[9]}")
gpu_trt_list=$(func_parser "${lines[10]}")
gpu_precision_list=$(func_parser "${lines[11]}")
log_path=$(func_parser "${lines[13]}")
status_log="${log_path}/result.log"
# install requirments
${python} -m pip install pynvml;
${python} -m pip install psutil;
${python} -m pip install GPUtil;
${python} -m pip install paddlesim==2.0.0
paddle_info="$(${python} -c "import paddle;print(f'paddle_version:{paddle.__version__}');print(f'paddle_commit:{paddle.__git_commit__}')")"
echo -e "\033[33m $paddle_info \033[0m" | tee -a ${status_log}
cpu_model=`cat /proc/cpuinfo | grep "model name" | awk -F ':' '{print $2}' | sort | uniq`
echo -e "\033[33m cpu_info:$cpu_model \033[0m" | tee -a ${status_log}
ip=`ifconfig| grep -A 1 'eth0'|grep 'inet'|awk -F ':' '{print $2}'|awk '{print $1}'`
echo -e "\033[33m ip_info:$ip \033[0m" | tee -a ${status_log}
function status_check(){
last_status=$1 # the exit code
run_model=$2
run_command=$3
run_log=$4
run_command=$2
run_log=$3
if [ $last_status -eq 0 ]; then
echo -e "\033[33m $run_model successfully with command - ${run_command}! \033[0m" | tee -a ${run_log}
echo -e "\033[33m Run successfully with command - ${run_command}! \033[0m" | tee -a ${run_log}
else
echo -e "\033[33m $case failed with command - ${run_command}! \033[0m" | tee -a ${run_log}
echo -e "\033[33m Run failed with command - ${run_command}! \033[0m" | tee -a ${run_log}
fi
}
IFS="|"
for train_model in ${train_model_list[*]}; do
if [ ${train_model} = "ocr_det" ];then
model_name="det"
yml_file="configs/det/det_mv3_db.yml"
elif [ ${train_model} = "ocr_rec" ];then
model_name="rec"
yml_file="configs/rec/rec_mv3_none_bilstm_ctc.yml"
else
model_name="det"
yml_file="configs/det/det_mv3_db.yml"
fi
IFS="|"
for gpu in ${gpu_list[*]}; do
use_gpu=True
if [ ${gpu} = "-1" ];then
use_gpu=False
env=""
elif [ ${#gpu} -le 1 ];then
env="CUDA_VISIBLE_DEVICES=${gpu}"
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 "${lines[4]}")
save_model_key=$(func_parser_key "${lines[5]}")
save_infer_key=$(func_parser_key "${lines[6]}")
train_batch_key=$(func_parser_key "${lines[7]}")
train_use_gpu_key=$(func_parser_key "${lines[8]}")
pretrain_model_key=$(func_parser_key "${lines[9]}")
trainer_list=$(func_parser_value "${lines[10]}")
norm_trainer=$(func_parser_value "${lines[11]}")
pact_trainer=$(func_parser_value "${lines[12]}")
fpgm_trainer=$(func_parser_value "${lines[13]}")
distill_trainer=$(func_parser_value "${lines[14]}")
eval_py=$(func_parser_value "${lines[15]}")
norm_export=$(func_parser_value "${lines[16]}")
pact_export=$(func_parser_value "${lines[17]}")
fpgm_export=$(func_parser_value "${lines[18]}")
distill_export=$(func_parser_value "${lines[19]}")
inference_py=$(func_parser_value "${lines[20]}")
use_gpu_key=$(func_parser_key "${lines[21]}")
use_gpu_list=$(func_parser_value "${lines[21]}")
use_mkldnn_key=$(func_parser_key "${lines[22]}")
use_mkldnn_list=$(func_parser_value "${lines[22]}")
cpu_threads_key=$(func_parser_key "${lines[23]}")
cpu_threads_list=$(func_parser_value "${lines[23]}")
batch_size_key=$(func_parser_key "${lines[24]}")
batch_size_list=$(func_parser_value "${lines[24]}")
use_trt_key=$(func_parser_key "${lines[25]}")
use_trt_list=$(func_parser_value "${lines[25]}")
precision_key=$(func_parser_key "${lines[26]}")
precision_list=$(func_parser_value "${lines[26]}")
model_dir_key=$(func_parser_key "${lines[27]}")
image_dir_key=$(func_parser_key "${lines[28]}")
save_log_key=$(func_parser_key "${lines[29]}")
LOG_PATH="./test/output"
mkdir -p ${LOG_PATH}
status_log="${LOG_PATH}/results.log"
if [ ${MODE} = "lite_train_infer" ]; then
export infer_img_dir="./train_data/icdar2015/text_localization/ch4_test_images/"
export epoch_num=10
elif [ ${MODE} = "whole_infer" ]; then
export infer_img_dir="./train_data/icdar2015/text_localization/ch4_test_images/"
export epoch_num=10
elif [ ${MODE} = "whole_train_infer" ]; then
export infer_img_dir="./train_data/icdar2015/text_localization/ch4_test_images/"
export epoch_num=300
else
export infer_img_dir="./inference/ch_det_data_50/all-sum-510"
export infer_model_dir="./inference/ch_ppocr_mobile_v2.0_det_train/best_accuracy"
fi
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_${batch_size}"
command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${cpu_threads_key}=${threads} ${model_dir_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir} ${save_log_key}=${_save_log_path}"
eval $command
status_check $? "${command}" "${status_log}"
done
done
done
else
IFS=","
array=(${gpu})
env="CUDA_VISIBLE_DEVICES=${array[0]}"
IFS="|"
fi
for auto_cast in ${auto_cast_list[*]}; do
for slim_trainer in ${slim_trainer_list[*]}; do
if [ ${slim_trainer} = "norm" ]; then
trainer="tools/train.py"
export_model="tools/export_model.py"
pretrain="./pretrain_models/MobileNetV3_large_x0_5_pretrained"
elif [ ${slim_trainer} = "pact" ]; then
trainer="deploy/slim/quantization/quant.py"
export_model="deploy/slim/quantization/export_model.py"
pretrain="./pretrain_models/det_mv3_db_v2.0_train/best_accuracy"
elif [ ${slim_trainer} = "fpgm" ]; then
trainer="deploy/slim/prune/sensitivity_anal.py"
export_model="deploy/slim/prune/export_prune_model.py"
pretrain="./pretrain_models/det_mv3_db_v2.0_train/best_accuracy"
wget -nc -P https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/sen.pickle
elif [ ${slim_trainer} = "distill" ]; then
trainer="deploy/slim/distill/train_dml.py"
export_model="deploy/slim/distill/export_distill_model.py"
pretrain=""
else
trainer="tools/train.py"
export_model="tools/export_model.py"
pretrain="./pretrain_models/MobileNetV3_large_x0_5_pretrained"
fi
save_log="${log_path}/${model_name}_${slim_trainer}_autocast_${auto_cast}_gpuid_${gpu}"
if [ ${#gpu} -le 2 ];then
command="${python} ${trainer} -c ${yml_file} -o Global.epoch_num=${epoch} Global.eval_batch_step=${eval_batch_step} Global.auto_cast=${auto_cast} Global.pretrained_model=${pretrain} Global.save_model_dir=${save_log} Global.use_gpu=${use_gpu} Train.loader.batch_size_per_card=2"
${python} ${trainer} -c ${yml_file} -o Global.epoch_num=${epoch} Global.eval_batch_step=${eval_batch_step} Global.auto_cast=${auto_cast} Global.pretrained_model=${pretrain} Global.save_model_dir=${save_log} Global.use_gpu=${use_gpu} Train.loader.batch_size_per_card=2
else
command="${python} -m paddle.distributed.launch --log_dir=./debug/ --gpus ${gpu} ${trainer} -c ${yml_file} -o Global.epoch_num=${epoch} Global.eval_batch_step=${eval_batch_step} Global.auto_cast=${auto_cast} Global.pretrained_model=${pretrain} Global.save_model_dir=${save_log} Global.use_gpu=${use_gpu} Train.loader.batch_size_per_card=2"
${python} -m paddle.distributed.launch --log_dir=./debug/ --gpus ${gpu} ${trainer} -c ${yml_file} -o Global.epoch_num=${epoch} Global.eval_batch_step=${eval_batch_step} Global.auto_cast=${auto_cast} Global.pretrained_model=${pretrain} Global.save_model_dir=${save_log} Global.use_gpu=${use_gpu} Train.loader.batch_size_per_card=2
fi
status_check $? "${trainer}" "${command}" "${status_log}"
command="${python} ${export_model} -c ${yml_file} -o Global.pretrained_model=${save_log}/latest Global.save_inference_dir=${save_log}_infer/ Global.save_model_dir=${save_log}"
${python} ${export_model} -c ${yml_file} -o Global.pretrained_model=${save_log}/latest Global.save_inference_dir=${save_log}_infer/ Global.save_model_dir=${save_log}
status_check $? "${trainer}" "${command}" "${status_log}"
if [ "${model_name}" = "det" ]; then
export rec_batch_size_list=( "1" )
inference="tools/infer/predict_det.py"
det_model_dir=${save_log}_infer
rec_model_dir=""
elif [ "${model_name}" = "rec" ]; then
inference="tools/infer/predict_rec.py"
rec_model_dir=${save_log}_infer
det_model_dir=""
fi
# inference
for device in ${devices[*]}; do
if [ ${device} = "cpu" ]; then
for use_mkldnn in ${use_mkldnn_list[*]}; do
for threads in ${cpu_threads_list[*]}; do
for rec_batch_size in ${rec_batch_size_list[*]}; do
save_log_path="${log_path}/${model_name}_${slim_trainer}_cpu_usemkldnn_${use_mkldnn}_cputhreads_${threads}_recbatchnum_${rec_batch_size}_infer.log"
command="${python} ${inference} --enable_mkldnn=${use_mkldnn} --use_gpu=False --cpu_threads=${threads} --benchmark=True --det_model_dir=${det_model_dir} --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir} --image_dir=${img_dir} --save_log_path=${save_log_path}"
${python} ${inference} --enable_mkldnn=${use_mkldnn} --use_gpu=False --cpu_threads=${threads} --benchmark=True --det_model_dir=${det_model_dir} --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir} --image_dir=${img_dir} --save_log_path=${save_log_path}
status_check $? "${inference}" "${command}" "${status_log}"
done
done
done
else
for use_trt in ${gpu_trt_list[*]}; do
for precision in ${gpu_precision_list[*]}; do
if [ ${use_trt} = "False" ] && [ ${precision} != "fp32" ]; then
continue
fi
for rec_batch_size in ${rec_batch_size_list[*]}; do
save_log_path="${log_path}/${model_name}_${slim_trainer}_gpu_usetensorrt_${use_trt}_usefp16_${precision}_recbatchnum_${rec_batch_size}_infer.log"
command="${python} ${inference} --use_gpu=True --use_tensorrt=${use_trt} --precision=${precision} --benchmark=True --det_model_dir=${det_model_dir} --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir} --image_dir=${img_dir} --save_log_path=${save_log_path}"
${python} ${inference} --use_gpu=True --use_tensorrt=${use_trt} --precision=${precision} --benchmark=True --det_model_dir=${det_model_dir} --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir} --image_dir=${img_dir} --save_log_path=${save_log_path}
status_check $? "${inference}" "${command}" "${status_log}"
done
done
done
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_${batch_size}"
command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_trt_key}=${use_trt} ${precision_key}=${precision} ${model_dir_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir} ${save_log_key}=${_save_log_path}"
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}"
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="|"
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 [ ${run_export} = "null" ]; then
continue
fi
save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
if [ ${#gpu} -le 2 ];then # epoch_num #TODO
cmd="${python} ${run_train} ${train_use_gpu_key}=${use_gpu} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num} ${save_model_key}=${save_log} "
elif [ ${#gpu} -le 15 ];then
cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num} ${save_model_key}=${save_log}"
else
cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num} ${save_model_key}=${save_log}"
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} ${pretrain_model_key}=${save_log}/latest ${save_infer_key}=${save_infer_path}"
eval $export_cmd
status_check $? "${export_cmd}" "${status_log}"
#run inference
save_infer_path="${save_log}"
func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${infer_img_dir}"
done
done
done
else
save_infer_path="${LOG_PATH}/${MODE}"
run_export=${norm_export}
export_cmd="${python} ${run_export} ${save_model_key}=${save_infer_path} ${pretrain_model_key}=${infer_model_dir} ${save_infer_key}=${save_infer_path}"
eval $export_cmd
status_check $? "${export_cmd}" "${status_log}"
#run inference
func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${infer_img_dir}"
fi
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