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Commit 03bb378f authored by LDOUBLEV's avatar LDOUBLEV
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fix TRT8 core bug

parents a2a12fe4 2e9abcb9
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Showing with 900 additions and 209 deletions
PTDN/test_inference_cpp.sh → test_tipc/test_inference_cpp.sh
View file @ 03bb378f
#!/bin/bash #!/bin/bash
source tests/common_func.sh source test_tipc/common_func.sh
FILENAME=$1 FILENAME=$1
dataline=$(awk 'NR==52, NR==66{print}' $FILENAME) dataline=$(awk 'NR==1, NR==16{print}' $FILENAME)
# parser params # parser params
IFS=$'\n' IFS=$'\n'
lines=(${dataline}) lines=(${dataline})
# parser cpp inference model # parser cpp inference model
use_opencv=$(func_parser_value "${lines[1]}") model_name=$(func_parser_value "${lines[1]}")
cpp_infer_model_dir_list=$(func_parser_value "${lines[2]}") use_opencv=$(func_parser_value "${lines[2]}")
cpp_infer_is_quant=$(func_parser_value "${lines[3]}") cpp_infer_model_dir_list=$(func_parser_value "${lines[3]}")
cpp_infer_is_quant=$(func_parser_value "${lines[4]}")
# parser cpp inference # parser cpp inference
inference_cmd=$(func_parser_value "${lines[4]}") inference_cmd=$(func_parser_value "${lines[5]}")
cpp_use_gpu_key=$(func_parser_key "${lines[5]}") cpp_use_gpu_key=$(func_parser_key "${lines[6]}")
cpp_use_gpu_list=$(func_parser_value "${lines[5]}") cpp_use_gpu_list=$(func_parser_value "${lines[6]}")
cpp_use_mkldnn_key=$(func_parser_key "${lines[6]}") cpp_use_mkldnn_key=$(func_parser_key "${lines[7]}")
cpp_use_mkldnn_list=$(func_parser_value "${lines[6]}") cpp_use_mkldnn_list=$(func_parser_value "${lines[7]}")
cpp_cpu_threads_key=$(func_parser_key "${lines[7]}") cpp_cpu_threads_key=$(func_parser_key "${lines[8]}")
cpp_cpu_threads_list=$(func_parser_value "${lines[7]}") cpp_cpu_threads_list=$(func_parser_value "${lines[8]}")
cpp_batch_size_key=$(func_parser_key "${lines[8]}") cpp_batch_size_key=$(func_parser_key "${lines[9]}")
cpp_batch_size_list=$(func_parser_value "${lines[8]}") cpp_batch_size_list=$(func_parser_value "${lines[9]}")
cpp_use_trt_key=$(func_parser_key "${lines[9]}") cpp_use_trt_key=$(func_parser_key "${lines[10]}")
cpp_use_trt_list=$(func_parser_value "${lines[9]}") cpp_use_trt_list=$(func_parser_value "${lines[10]}")
cpp_precision_key=$(func_parser_key "${lines[10]}") cpp_precision_key=$(func_parser_key "${lines[11]}")
cpp_precision_list=$(func_parser_value "${lines[10]}") cpp_precision_list=$(func_parser_value "${lines[11]}")
cpp_infer_model_key=$(func_parser_key "${lines[11]}") cpp_infer_model_key=$(func_parser_key "${lines[12]}")
cpp_image_dir_key=$(func_parser_key "${lines[12]}") cpp_image_dir_key=$(func_parser_key "${lines[13]}")
cpp_infer_img_dir=$(func_parser_value "${lines[12]}") cpp_infer_img_dir=$(func_parser_value "${lines[13]}")
cpp_infer_key1=$(func_parser_key "${lines[13]}") cpp_infer_key1=$(func_parser_key "${lines[14]}")
cpp_infer_value1=$(func_parser_value "${lines[13]}") cpp_infer_value1=$(func_parser_value "${lines[14]}")
cpp_benchmark_key=$(func_parser_key "${lines[14]}") cpp_benchmark_key=$(func_parser_key "${lines[15]}")
cpp_benchmark_value=$(func_parser_value "${lines[14]}") cpp_benchmark_value=$(func_parser_value "${lines[15]}")
LOG_PATH="./test_tipc/output"
LOG_PATH="./tests/output"
mkdir -p ${LOG_PATH} mkdir -p ${LOG_PATH}
status_log="${LOG_PATH}/results_cpp.log" status_log="${LOG_PATH}/results_cpp.log"
......
test_tipc/test_inference_jeston.sh 0 → 100644
View file @ 03bb378f
#!/bin/bash
source test_tipc/common_func.sh
source test_tipc/test_train_inference_python.sh
FILENAME=$1
# MODE be one of ['whole_infer']
MODE=$2
dataline=$(awk 'NR==1, NR==17{print}' $FILENAME)
# parser params
IFS=$'\n'
lines=(${dataline})
model_name=$(func_parser_value "${lines[1]}")
python=$(func_parser_value "${lines[2]}")
infer_model_dir_list=$(func_parser_value "${lines[3]}")
infer_export_list=$(func_parser_value "${lines[4]}")
infer_is_quant=$(func_parser_value "${lines[5]}")
# parser inference
inference_py=$(func_parser_value "${lines[6]}")
use_gpu_key=$(func_parser_key "${lines[7]}")
use_gpu_list=$(func_parser_value "${lines[7]}")
use_mkldnn_key=$(func_parser_key "${lines[8]}")
use_mkldnn_list=$(func_parser_value "${lines[8]}")
cpu_threads_key=$(func_parser_key "${lines[9]}")
cpu_threads_list=$(func_parser_value "${lines[9]}")
batch_size_key=$(func_parser_key "${lines[10]}")
batch_size_list=$(func_parser_value "${lines[10]}")
use_trt_key=$(func_parser_key "${lines[11]}")
use_trt_list=$(func_parser_value "${lines[11]}")
precision_key=$(func_parser_key "${lines[12]}")
precision_list=$(func_parser_value "${lines[12]}")
infer_model_key=$(func_parser_key "${lines[13]}")
image_dir_key=$(func_parser_key "${lines[14]}")
infer_img_dir=$(func_parser_value "${lines[14]}")
save_log_key=$(func_parser_key "${lines[15]}")
benchmark_key=$(func_parser_key "${lines[16]}")
benchmark_value=$(func_parser_value "${lines[16]}")
infer_key1=$(func_parser_key "${lines[17]}")
infer_value1=$(func_parser_value "${lines[17]}")
LOG_PATH="./test_tipc/output"
mkdir -p ${LOG_PATH}
status_log="${LOG_PATH}/results_python.log"
if [ ${MODE} = "whole_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} ${infer_run_exports[Count]} ${set_export_weight} ${set_save_infer_key}"
echo ${infer_run_exports[Count]}
echo $export_cmd
eval $export_cmd
status_export=$?
status_check $status_export "${export_cmd}" "${status_log}"
else
save_infer_dir=${infer_model}
fi
#run inference
is_quant=${infer_quant_flag[Count]}
if [ ${MODE} = "klquant_infer" ]; then
is_quant="True"
fi
func_inference "${python}" "${inference_py}" "${save_infer_dir}" "${LOG_PATH}" "${infer_img_dir}" ${is_quant}
Count=$(($Count + 1))
done
fi
test_tipc/test_lite_arm_cpp.sh 0 → 100644
View file @ 03bb378f
#!/bin/bash
source ./common_func.sh
export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
FILENAME=$1
dataline=$(cat $FILENAME)
# parser params
IFS=$'\n'
lines=(${dataline})
# parser lite inference
inference_cmd=$(func_parser_value "${lines[1]}")
runtime_device=$(func_parser_value "${lines[2]}")
det_model_list=$(func_parser_value "${lines[3]}")
rec_model_list=$(func_parser_value "${lines[4]}")
cls_model_list=$(func_parser_value "${lines[5]}")
cpu_threads_list=$(func_parser_value "${lines[6]}")
det_batch_size_list=$(func_parser_value "${lines[7]}")
rec_batch_size_list=$(func_parser_value "${lines[8]}")
infer_img_dir_list=$(func_parser_value "${lines[9]}")
config_dir=$(func_parser_value "${lines[10]}")
rec_dict_dir=$(func_parser_value "${lines[11]}")
benchmark_value=$(func_parser_value "${lines[12]}")
if [[ $inference_cmd =~ "det" ]]; then
lite_model_list=${det_lite_model_list}
elif [[ $inference_cmd =~ "rec" ]]; then
lite_model_list=(${rec_lite_model_list[*]} ${cls_lite_model_list[*]})
elif [[ $inference_cmd =~ "system" ]]; then
lite_model_list=(${det_lite_model_list[*]} ${rec_lite_model_list[*]} ${cls_lite_model_list[*]})
else
echo "inference_cmd is wrong, please check."
exit 1
fi
LOG_PATH="./output"
mkdir -p ${LOG_PATH}
status_log="${LOG_PATH}/results.log"
function func_test_det(){
IFS='|'
_script=$1
_det_model=$2
_log_path=$3
_img_dir=$4
_config=$5
if [[ $_det_model =~ "slim" ]]; then
precision="INT8"
else
precision="FP32"
fi
# lite inference
for num_threads in ${cpu_threads_list[*]}; do
for det_batchsize in ${det_batch_size_list[*]}; do
_save_log_path="${_log_path}/lite_${_det_model}_runtime_device_${runtime_device}_precision_${precision}_det_batchsize_${det_batchsize}_threads_${num_threads}.log"
command="${_script} ${_det_model} ${runtime_device} ${precision} ${num_threads} ${det_batchsize} ${_img_dir} ${_config} ${benchmark_value} > ${_save_log_path} 2>&1"
eval ${command}
status_check $? "${command}" "${status_log}"
done
done
}
function func_test_rec(){
IFS='|'
_script=$1
_rec_model=$2
_cls_model=$3
_log_path=$4
_img_dir=$5
_config=$6
_rec_dict_dir=$7
if [[ $_det_model =~ "slim" ]]; then
_precision="INT8"
else
_precision="FP32"
fi
# lite inference
for num_threads in ${cpu_threads_list[*]}; do
for rec_batchsize in ${rec_batch_size_list[*]}; do
_save_log_path="${_log_path}/lite_${_rec_model}_${cls_model}_runtime_device_${runtime_device}_precision_${_precision}_rec_batchsize_${rec_batchsize}_threads_${num_threads}.log"
command="${_script} ${_rec_model} ${_cls_model} ${runtime_device} ${_precision} ${num_threads} ${rec_batchsize} ${_img_dir} ${_config} ${_rec_dict_dir} ${benchmark_value} > ${_save_log_path} 2>&1"
eval ${command}
status_check $? "${command}" "${status_log}"
done
done
}
function func_test_system(){
IFS='|'
_script=$1
_det_model=$2
_rec_model=$3
_cls_model=$4
_log_path=$5
_img_dir=$6
_config=$7
_rec_dict_dir=$8
if [[ $_det_model =~ "slim" ]]; then
_precision="INT8"
else
_precision="FP32"
fi
# lite inference
for num_threads in ${cpu_threads_list[*]}; do
for det_batchsize in ${det_batch_size_list[*]}; do
for rec_batchsize in ${rec_batch_size_list[*]}; do
_save_log_path="${_log_path}/lite_${_det_model}_${_rec_model}_${_cls_model}_runtime_device_${runtime_device}_precision_${_precision}_det_batchsize_${det_batchsize}_rec_batchsize_${rec_batchsize}_threads_${num_threads}.log"
command="${_script} ${_det_model} ${_rec_model} ${_cls_model} ${runtime_device} ${_precision} ${num_threads} ${det_batchsize} ${_img_dir} ${_config} ${_rec_dict_dir} ${benchmark_value} > ${_save_log_path} 2>&1"
eval ${command}
status_check $? "${command}" "${status_log}"
done
done
done
}
echo "################### run test ###################"
if [[ $inference_cmd =~ "det" ]]; then
IFS="|"
det_model_list=(${det_model_list[*]})
for i in {0..1}; do
#run lite inference
for img_dir in ${infer_img_dir_list[*]}; do
func_test_det "${inference_cmd}" "${det_model_list[i]}_opt.nb" "${LOG_PATH}" "${img_dir}" "${config_dir}"
done
done
elif [[ $inference_cmd =~ "rec" ]]; then
IFS="|"
rec_model_list=(${rec_model_list[*]})
cls_model_list=(${cls_model_list[*]})
for i in {0..1}; do
#run lite inference
for img_dir in ${infer_img_dir_list[*]}; do
func_test_rec "${inference_cmd}" "${rec_model}_opt.nb" "${cls_model_list[i]}_opt.nb" "${LOG_PATH}" "${img_dir}" "${rec_dict_dir}" "${config_dir}"
done
done
elif [[ $inference_cmd =~ "system" ]]; then
IFS="|"
det_model_list=(${det_model_list[*]})
rec_model_list=(${rec_model_list[*]})
cls_model_list=(${cls_model_list[*]})
for i in {0..1}; do
#run lite inference
for img_dir in ${infer_img_dir_list[*]}; do
func_test_system "${inference_cmd}" "${det_model_list[i]}_opt.nb" "${rec_model_list[i]}_opt.nb" "${cls_model_list[i]}_opt.nb" "${LOG_PATH}" "${img_dir}" "${config_dir}" "${rec_dict_dir}"
done
done
fi
test_tipc/test_paddle2onnx.sh 0 → 100644
View file @ 03bb378f
#!/bin/bash
source test_tipc/common_func.sh
FILENAME=$1
dataline=$(cat ${FILENAME})
lines=(${dataline})
# common params
model_name=$(func_parser_value "${lines[1]}")
python=$(func_parser_value "${lines[2]}")
# parser params
dataline=$(awk 'NR==1, NR==12{print}' $FILENAME)
IFS=$'\n'
lines=(${dataline})
# parser paddle2onnx
model_name=$(func_parser_value "${lines[1]}")
python=$(func_parser_value "${lines[2]}")
padlle2onnx_cmd=$(func_parser_value "${lines[3]}")
infer_model_dir_key=$(func_parser_key "${lines[4]}")
infer_model_dir_value=$(func_parser_value "${lines[4]}")
model_filename_key=$(func_parser_key "${lines[5]}")
model_filename_value=$(func_parser_value "${lines[5]}")
params_filename_key=$(func_parser_key "${lines[6]}")
params_filename_value=$(func_parser_value "${lines[6]}")
save_file_key=$(func_parser_key "${lines[7]}")
save_file_value=$(func_parser_value "${lines[7]}")
opset_version_key=$(func_parser_key "${lines[8]}")
opset_version_value=$(func_parser_value "${lines[8]}")
enable_onnx_checker_key=$(func_parser_key "${lines[9]}")
enable_onnx_checker_value=$(func_parser_value "${lines[9]}")
# parser onnx inference
inference_py=$(func_parser_value "${lines[10]}")
use_gpu_key=$(func_parser_key "${lines[11]}")
use_gpu_value=$(func_parser_value "${lines[11]}")
det_model_key=$(func_parser_key "${lines[12]}")
image_dir_key=$(func_parser_key "${lines[13]}")
image_dir_value=$(func_parser_value "${lines[13]}")
LOG_PATH="./test_tipc/output"
mkdir -p ./test_tipc/output
status_log="${LOG_PATH}/results_paddle2onnx.log"
function func_paddle2onnx(){
IFS='|'
_script=$1
# paddle2onnx
_save_log_path="${LOG_PATH}/paddle2onnx_infer_cpu.log"
set_dirname=$(func_set_params "${infer_model_dir_key}" "${infer_model_dir_value}")
set_model_filename=$(func_set_params "${model_filename_key}" "${model_filename_value}")
set_params_filename=$(func_set_params "${params_filename_key}" "${params_filename_value}")
set_save_model=$(func_set_params "${save_file_key}" "${save_file_value}")
set_opset_version=$(func_set_params "${opset_version_key}" "${opset_version_value}")
set_enable_onnx_checker=$(func_set_params "${enable_onnx_checker_key}" "${enable_onnx_checker_value}")
trans_model_cmd="${padlle2onnx_cmd} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_save_model} ${set_opset_version} ${set_enable_onnx_checker}"
eval $trans_model_cmd
last_status=${PIPESTATUS[0]}
status_check $last_status "${trans_model_cmd}" "${status_log}"
# python inference
set_gpu=$(func_set_params "${use_gpu_key}" "${use_gpu_value}")
set_model_dir=$(func_set_params "${det_model_key}" "${save_file_value}")
set_img_dir=$(func_set_params "${image_dir_key}" "${image_dir_value}")
infer_model_cmd="${python} ${inference_py} ${set_gpu} ${set_img_dir} ${set_model_dir} --use_onnx=True > ${_save_log_path} 2>&1 "
eval $infer_model_cmd
status_check $last_status "${infer_model_cmd}" "${status_log}"
}
echo "################### run test ###################"
export Count=0
IFS="|"
func_paddle2onnx
\ No newline at end of file
PTDN/test_serving.sh → test_tipc/test_serving.sh
View file @ 03bb378f
#!/bin/bash #!/bin/bash
source PTDN/common_func.sh source test_tipc/common_func.sh
FILENAME=$1 FILENAME=$1
dataline=$(awk 'NR==67, NR==83{print}' $FILENAME) dataline=$(awk 'NR==1, NR==18{print}' $FILENAME)
# parser params # parser params
IFS=$'\n' IFS=$'\n'
...@@ -35,9 +35,11 @@ web_use_trt_list=$(func_parser_value "${lines[14]}") ...@@ -35,9 +35,11 @@ web_use_trt_list=$(func_parser_value "${lines[14]}")
web_precision_key=$(func_parser_key "${lines[15]}") web_precision_key=$(func_parser_key "${lines[15]}")
web_precision_list=$(func_parser_value "${lines[15]}") web_precision_list=$(func_parser_value "${lines[15]}")
pipeline_py=$(func_parser_value "${lines[16]}") pipeline_py=$(func_parser_value "${lines[16]}")
image_dir_key=$(func_parser_key "${lines[17]}")
image_dir_value=$(func_parser_value "${lines[17]}")
LOG_PATH="../../PTDN/output" LOG_PATH="../../test_tipc/output"
mkdir -p ./PTDN/output mkdir -p ./test_tipc/output
status_log="${LOG_PATH}/results_serving.log" status_log="${LOG_PATH}/results_serving.log"
function func_serving(){ function func_serving(){
...@@ -51,67 +53,98 @@ function func_serving(){ ...@@ -51,67 +53,98 @@ function func_serving(){
set_params_filename=$(func_set_params "${params_filename_key}" "${params_filename_value}") set_params_filename=$(func_set_params "${params_filename_key}" "${params_filename_value}")
set_serving_server=$(func_set_params "${serving_server_key}" "${serving_server_value}") set_serving_server=$(func_set_params "${serving_server_key}" "${serving_server_value}")
set_serving_client=$(func_set_params "${serving_client_key}" "${serving_client_value}") set_serving_client=$(func_set_params "${serving_client_key}" "${serving_client_value}")
set_image_dir=$(func_set_params "${image_dir_key}" "${image_dir_value}")
trans_model_cmd="${python} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}" trans_model_cmd="${python} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
eval $trans_model_cmd eval $trans_model_cmd
cd ${serving_dir_value} cd ${serving_dir_value}
echo $PWD echo $PWD
unset https_proxy unset https_proxy
unset http_proxy unset http_proxy
for use_gpu in ${web_use_gpu_list[*]}; do for python in ${python[*]}; do
echo ${ues_gpu} if [ ${python} = "cpp"]; then
if [ ${use_gpu} = "null" ]; then for use_gpu in ${web_use_gpu_list[*]}; do
for use_mkldnn in ${web_use_mkldnn_list[*]}; do if [ ${use_gpu} = "null" ]; then
if [ ${use_mkldnn} = "False" ]; then web_service_cpp_cmd="${python} -m paddle_serving_server.serve --model ppocr_det_mobile_2.0_serving/ ppocr_rec_mobile_2.0_serving/ --port 9293"
continue
fi
for threads in ${web_cpu_threads_list[*]}; do
_save_log_path="${LOG_PATH}/server_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_1.log"
set_cpu_threads=$(func_set_params "${web_cpu_threads_key}" "${threads}")
web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}=${use_gpu} ${web_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} &"
eval $web_service_cmd
sleep 2s
pipeline_cmd="${python} ${pipeline_py} > ${_save_log_path} 2>&1 "
eval $pipeline_cmd
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${pipeline_cmd}" "${status_log}"
PID=$!
kill $PID
sleep 2s
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
done
done
elif [ ${use_gpu} = "0" ]; then
for use_trt in ${web_use_trt_list[*]}; do
for precision in ${web_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" ]] && [[ ${_flag_quant} = "True" ]]; then
continue
fi
_save_log_path="${LOG_PATH}/server_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_1.log"
set_tensorrt=$(func_set_params "${web_use_trt_key}" "${use_trt}")
set_precision=$(func_set_params "${web_precision_key}" "${precision}")
web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} & "
eval $web_service_cmd eval $web_service_cmd
sleep 2s sleep 2s
pipeline_cmd="${python} ${pipeline_py} > ${_save_log_path} 2>&1" _save_log_path="${LOG_PATH}/server_infer_cpp_cpu_pipeline_usemkldnn_False_threads_4_batchsize_1.log"
pipeline_cmd="${python} ocr_cpp_client.py ppocr_det_mobile_2.0_client/ ppocr_rec_mobile_2.0_client/"
eval $pipeline_cmd eval $pipeline_cmd
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${pipeline_cmd}" "${status_log}" status_check $last_status "${pipeline_cmd}" "${status_log}"
PID=$!
kill $PID
sleep 2s sleep 2s
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9 ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
done else
web_service_cpp_cmd="${python} -m paddle_serving_server.serve --model ppocr_det_mobile_2.0_serving/ ppocr_rec_mobile_2.0_serving/ --port 9293 --gpu_id=0"
eval $web_service_cmd
sleep 2s
_save_log_path="${LOG_PATH}/server_infer_cpp_cpu_pipeline_usemkldnn_False_threads_4_batchsize_1.log"
pipeline_cmd="${python} ocr_cpp_client.py ppocr_det_mobile_2.0_client/ ppocr_rec_mobile_2.0_client/"
eval $pipeline_cmd
status_check $last_status "${pipeline_cmd}" "${status_log}"
sleep 2s
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
fi
done done
else else
echo "Does not support hardware other than CPU and GPU Currently!" # python serving
for use_gpu in ${web_use_gpu_list[*]}; do
echo ${ues_gpu}
if [ ${use_gpu} = "null" ]; then
for use_mkldnn in ${web_use_mkldnn_list[*]}; do
if [ ${use_mkldnn} = "False" ]; then
continue
fi
for threads in ${web_cpu_threads_list[*]}; do
set_cpu_threads=$(func_set_params "${web_cpu_threads_key}" "${threads}")
web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}=${use_gpu} ${web_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} &"
eval $web_service_cmd
sleep 2s
for pipeline in ${pipeline_py[*]}; do
_save_log_path="${LOG_PATH}/server_infer_cpu_${pipeline%_client*}_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_1.log"
pipeline_cmd="${python} ${pipeline} ${set_image_dir} > ${_save_log_path} 2>&1 "
eval $pipeline_cmd
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${pipeline_cmd}" "${status_log}"
sleep 2s
done
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
done
done
elif [ ${use_gpu} = "0" ]; then
for use_trt in ${web_use_trt_list[*]}; do
for precision in ${web_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" ]] && [[ ${_flag_quant} = "True" ]]; then
continue
fi
set_tensorrt=$(func_set_params "${web_use_trt_key}" "${use_trt}")
set_precision=$(func_set_params "${web_precision_key}" "${precision}")
web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} & "
eval $web_service_cmd
sleep 2s
for pipeline in ${pipeline_py[*]}; do
_save_log_path="${LOG_PATH}/server_infer_gpu_${pipeline%_client*}_usetrt_${use_trt}_precision_${precision}_batchsize_1.log"
pipeline_cmd="${python} ${pipeline} ${set_image_dir}> ${_save_log_path} 2>&1"
eval $pipeline_cmd
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${pipeline_cmd}" "${status_log}"
sleep 2s
done
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
done
done
else
echo "Does not support hardware other than CPU and GPU Currently!"
fi
done
fi fi
done done
} }
......
PTDN/test_train_inference_python.sh → test_tipc/test_train_inference_python.sh
View file @ 03bb378f
#!/bin/bash #!/bin/bash
source tests/common_func.sh source test_tipc/common_func.sh
FILENAME=$1 FILENAME=$1
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer', 'klquant_infer'] # MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer', 'whole_infer', 'klquant_whole_infer']
MODE=$2 MODE=$2
dataline=$(awk 'NR==1, NR==51{print}' $FILENAME) dataline=$(awk 'NR==1, NR==51{print}' $FILENAME)
...@@ -59,6 +59,7 @@ export_key1=$(func_parser_key "${lines[33]}") ...@@ -59,6 +59,7 @@ export_key1=$(func_parser_key "${lines[33]}")
export_value1=$(func_parser_value "${lines[33]}") export_value1=$(func_parser_value "${lines[33]}")
export_key2=$(func_parser_key "${lines[34]}") export_key2=$(func_parser_key "${lines[34]}")
export_value2=$(func_parser_value "${lines[34]}") export_value2=$(func_parser_value "${lines[34]}")
inference_dir=$(func_parser_value "${lines[35]}")
# parser inference model # parser inference model
infer_model_dir_list=$(func_parser_value "${lines[36]}") infer_model_dir_list=$(func_parser_value "${lines[36]}")
...@@ -88,38 +89,40 @@ infer_key1=$(func_parser_key "${lines[50]}") ...@@ -88,38 +89,40 @@ infer_key1=$(func_parser_key "${lines[50]}")
infer_value1=$(func_parser_value "${lines[50]}") infer_value1=$(func_parser_value "${lines[50]}")
# parser klquant_infer # parser klquant_infer
if [ ${MODE} = "klquant_infer" ]; then if [ ${MODE} = "klquant_whole_infer" ]; then
dataline=$(awk 'NR==82, NR==98{print}' $FILENAME) dataline=$(awk 'NR==1 NR==17{print}' $FILENAME)
lines=(${dataline}) lines=(${dataline})
model_name=$(func_parser_value "${lines[1]}")
python=$(func_parser_value "${lines[2]}")
# parser inference model # parser inference model
infer_model_dir_list=$(func_parser_value "${lines[1]}") infer_model_dir_list=$(func_parser_value "${lines[3]}")
infer_export_list=$(func_parser_value "${lines[2]}") infer_export_list=$(func_parser_value "${lines[4]}")
infer_is_quant=$(func_parser_value "${lines[3]}") infer_is_quant=$(func_parser_value "${lines[5]}")
# parser inference # parser inference
inference_py=$(func_parser_value "${lines[4]}") inference_py=$(func_parser_value "${lines[6]}")
use_gpu_key=$(func_parser_key "${lines[5]}") use_gpu_key=$(func_parser_key "${lines[7]}")
use_gpu_list=$(func_parser_value "${lines[5]}") use_gpu_list=$(func_parser_value "${lines[7]}")
use_mkldnn_key=$(func_parser_key "${lines[6]}") use_mkldnn_key=$(func_parser_key "${lines[8]}")
use_mkldnn_list=$(func_parser_value "${lines[6]}") use_mkldnn_list=$(func_parser_value "${lines[8]}")
cpu_threads_key=$(func_parser_key "${lines[7]}") cpu_threads_key=$(func_parser_key "${lines[9]}")
cpu_threads_list=$(func_parser_value "${lines[7]}") cpu_threads_list=$(func_parser_value "${lines[9]}")
batch_size_key=$(func_parser_key "${lines[8]}") batch_size_key=$(func_parser_key "${lines[10]}")
batch_size_list=$(func_parser_value "${lines[8]}") batch_size_list=$(func_parser_value "${lines[10]}")
use_trt_key=$(func_parser_key "${lines[9]}") use_trt_key=$(func_parser_key "${lines[11]}")
use_trt_list=$(func_parser_value "${lines[9]}") use_trt_list=$(func_parser_value "${lines[11]}")
precision_key=$(func_parser_key "${lines[10]}") precision_key=$(func_parser_key "${lines[12]}")
precision_list=$(func_parser_value "${lines[10]}") precision_list=$(func_parser_value "${lines[12]}")
infer_model_key=$(func_parser_key "${lines[11]}") infer_model_key=$(func_parser_key "${lines[13]}")
image_dir_key=$(func_parser_key "${lines[12]}") image_dir_key=$(func_parser_key "${lines[14]}")
infer_img_dir=$(func_parser_value "${lines[12]}") infer_img_dir=$(func_parser_value "${lines[14]}")
save_log_key=$(func_parser_key "${lines[13]}") save_log_key=$(func_parser_key "${lines[15]}")
benchmark_key=$(func_parser_key "${lines[14]}") benchmark_key=$(func_parser_key "${lines[16]}")
benchmark_value=$(func_parser_value "${lines[14]}") benchmark_value=$(func_parser_value "${lines[16]}")
infer_key1=$(func_parser_key "${lines[15]}") infer_key1=$(func_parser_key "${lines[17]}")
infer_value1=$(func_parser_value "${lines[15]}") infer_value1=$(func_parser_value "${lines[17]}")
fi fi
LOG_PATH="./tests/output" LOG_PATH="./test_tipc/output"
mkdir -p ${LOG_PATH} mkdir -p ${LOG_PATH}
status_log="${LOG_PATH}/results_python.log" status_log="${LOG_PATH}/results_python.log"
...@@ -202,7 +205,7 @@ function func_inference(){ ...@@ -202,7 +205,7 @@ function func_inference(){
done done
} }
if [ ${MODE} = "infer" ] || [ ${MODE} = "klquant_infer" ]; then if [ ${MODE} = "whole_infer" ] || [ ${MODE} = "klquant_whole_infer" ]; then
GPUID=$3 GPUID=$3
if [ ${#GPUID} -le 0 ];then if [ ${#GPUID} -le 0 ];then
env=" " env=" "
...@@ -243,7 +246,7 @@ else ...@@ -243,7 +246,7 @@ else
export Count=0 export Count=0
USE_GPU_KEY=(${train_use_gpu_value}) USE_GPU_KEY=(${train_use_gpu_value})
for gpu in ${gpu_list[*]}; do for gpu in ${gpu_list[*]}; do
use_gpu=${USE_GPU_KEY[Count]} train_use_gpu=${USE_GPU_KEY[Count]}
Count=$(($Count + 1)) Count=$(($Count + 1))
ips="" ips=""
if [ ${gpu} = "-1" ];then if [ ${gpu} = "-1" ];then
...@@ -301,11 +304,20 @@ else ...@@ -301,11 +304,20 @@ else
set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}") set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}")
set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_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_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}")
set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${use_gpu}") set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${train_use_gpu}")
save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}" if [ ${#ips} -le 26 ];then
save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
nodes=1
else
IFS=","
ips_array=(${ips})
IFS="|"
nodes=${#ips_array[@]}
save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}"
fi
# load pretrain from norm training if current trainer is pact or fpgm trainer # load pretrain from norm training if current trainer is pact or fpgm trainer
if [ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]; then if ([ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]) && [ ${nodes} -le 1 ]; then
set_pretrain="${load_norm_train_model}" set_pretrain="${load_norm_train_model}"
fi fi
...@@ -315,7 +327,7 @@ else ...@@ -315,7 +327,7 @@ else
elif [ ${#ips} -le 26 ];then # train with multi-gpu elif [ ${#ips} -le 26 ];then # train with multi-gpu
cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}" cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
else # train with multi-machine else # train with multi-machine
cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${set_use_gpu} ${run_train} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}" cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
fi fi
# run train # run train
eval "unset CUDA_VISIBLE_DEVICES" eval "unset CUDA_VISIBLE_DEVICES"
...@@ -324,7 +336,7 @@ else ...@@ -324,7 +336,7 @@ else
set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}") set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}")
# save norm trained models to set pretrain for pact training and fpgm training # save norm trained models to set pretrain for pact training and fpgm training
if [ ${trainer} = ${trainer_norm} ]; then if [ ${trainer} = ${trainer_norm} ] && [ ${nodes} -le 1]; then
load_norm_train_model=${set_eval_pretrain} load_norm_train_model=${set_eval_pretrain}
fi fi
# run eval # run eval
...@@ -347,7 +359,13 @@ else ...@@ -347,7 +359,13 @@ else
#run inference #run inference
eval $env eval $env
save_infer_path="${save_log}" save_infer_path="${save_log}"
func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}" if [ ${inference_dir} != "null" ] && [ ${inference_dir} != '##' ]; then
infer_model_dir="${save_infer_path}/${inference_dir}"
else
infer_model_dir=${save_infer_path}
fi
func_inference "${python}" "${inference_py}" "${infer_model_dir}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}"
eval "unset CUDA_VISIBLE_DEVICES" eval "unset CUDA_VISIBLE_DEVICES"
fi fi
done # done with: for trainer in ${trainer_list[*]}; do done # done with: for trainer in ${trainer_list[*]}; do
......
tools/__init__.py 0 → 100644
View file @ 03bb378f
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
tools/eval.py
View file @ 03bb378f
...@@ -27,7 +27,7 @@ from ppocr.data import build_dataloader ...@@ -27,7 +27,7 @@ from ppocr.data import build_dataloader
from ppocr.modeling.architectures import build_model from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process from ppocr.postprocess import build_post_process
from ppocr.metrics import build_metric from ppocr.metrics import build_metric
from ppocr.utils.save_load import init_model, load_dygraph_params from ppocr.utils.save_load import load_model
from ppocr.utils.utility import print_dict from ppocr.utils.utility import print_dict
import tools.program as program import tools.program as program
...@@ -60,7 +60,7 @@ def main(): ...@@ -60,7 +60,7 @@ def main():
else: else:
model_type = None model_type = None
best_model_dict = load_dygraph_params(config, model, logger, None) best_model_dict = load_model(config, model)
if len(best_model_dict): if len(best_model_dict):
logger.info('metric in ckpt ***************') logger.info('metric in ckpt ***************')
for k, v in best_model_dict.items(): for k, v in best_model_dict.items():
......
tools/export_center.py
View file @ 03bb378f
...@@ -27,7 +27,7 @@ sys.path.append(os.path.abspath(os.path.join(__dir__, '..'))) ...@@ -27,7 +27,7 @@ sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
from ppocr.data import build_dataloader from ppocr.data import build_dataloader
from ppocr.modeling.architectures import build_model from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model, load_dygraph_params from ppocr.utils.save_load import load_model
from ppocr.utils.utility import print_dict from ppocr.utils.utility import print_dict
import tools.program as program import tools.program as program
...@@ -57,7 +57,7 @@ def main(): ...@@ -57,7 +57,7 @@ def main():
model = build_model(config['Architecture']) model = build_model(config['Architecture'])
best_model_dict = load_dygraph_params(config, model, logger, None) best_model_dict = load_model(config, model)
if len(best_model_dict): if len(best_model_dict):
logger.info('metric in ckpt ***************') logger.info('metric in ckpt ***************')
for k, v in best_model_dict.items(): for k, v in best_model_dict.items():
......
tools/export_model.py
View file @ 03bb378f
...@@ -26,7 +26,7 @@ from paddle.jit import to_static ...@@ -26,7 +26,7 @@ from paddle.jit import to_static
from ppocr.modeling.architectures import build_model from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model from ppocr.utils.save_load import load_model
from ppocr.utils.logging import get_logger from ppocr.utils.logging import get_logger
from tools.program import load_config, merge_config, ArgsParser from tools.program import load_config, merge_config, ArgsParser
...@@ -107,7 +107,7 @@ def main(): ...@@ -107,7 +107,7 @@ def main():
else: # base rec model else: # base rec model
config["Architecture"]["Head"]["out_channels"] = char_num config["Architecture"]["Head"]["out_channels"] = char_num
model = build_model(config["Architecture"]) model = build_model(config["Architecture"])
init_model(config, model) load_model(config, model)
model.eval() model.eval()
save_path = config["Global"]["save_inference_dir"] save_path = config["Global"]["save_inference_dir"]
......
tools/infer/predict_cls.py
View file @ 03bb378f
...@@ -47,6 +47,7 @@ class TextClassifier(object): ...@@ -47,6 +47,7 @@ class TextClassifier(object):
self.postprocess_op = build_post_process(postprocess_params) self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors, _ = \ self.predictor, self.input_tensor, self.output_tensors, _ = \
utility.create_predictor(args, 'cls', logger) utility.create_predictor(args, 'cls', logger)
self.use_onnx = args.use_onnx
def resize_norm_img(self, img): def resize_norm_img(self, img):
imgC, imgH, imgW = self.cls_image_shape imgC, imgH, imgW = self.cls_image_shape
...@@ -100,10 +101,16 @@ class TextClassifier(object): ...@@ -100,10 +101,16 @@ class TextClassifier(object):
norm_img_batch = np.concatenate(norm_img_batch) norm_img_batch = np.concatenate(norm_img_batch)
norm_img_batch = norm_img_batch.copy() norm_img_batch = norm_img_batch.copy()
self.input_tensor.copy_from_cpu(norm_img_batch) if self.use_onnx:
self.predictor.run() input_dict = {}
prob_out = self.output_tensors[0].copy_to_cpu() input_dict[self.input_tensor.name] = norm_img_batch
self.predictor.try_shrink_memory() outputs = self.predictor.run(self.output_tensors, input_dict)
prob_out = outputs[0]
else:
self.input_tensor.copy_from_cpu(norm_img_batch)
self.predictor.run()
prob_out = self.output_tensors[0].copy_to_cpu()
self.predictor.try_shrink_memory()
cls_result = self.postprocess_op(prob_out) cls_result = self.postprocess_op(prob_out)
elapse += time.time() - starttime elapse += time.time() - starttime
for rno in range(len(cls_result)): for rno in range(len(cls_result)):
......
tools/infer/predict_det.py
View file @ 03bb378f
...@@ -38,6 +38,7 @@ class TextDetector(object): ...@@ -38,6 +38,7 @@ class TextDetector(object):
def __init__(self, args): def __init__(self, args):
self.args = args self.args = args
self.det_algorithm = args.det_algorithm self.det_algorithm = args.det_algorithm
self.use_onnx = args.use_onnx
pre_process_list = [{ pre_process_list = [{
'DetResizeForTest': { 'DetResizeForTest': {
'limit_side_len': args.det_limit_side_len, 'limit_side_len': args.det_limit_side_len,
...@@ -100,7 +101,12 @@ class TextDetector(object): ...@@ -100,7 +101,12 @@ class TextDetector(object):
else: else:
logger.info("unknown det_algorithm:{}".format(self.det_algorithm)) logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
sys.exit(0) sys.exit(0)
if self.use_onnx:
pre_process_list[0] = {
'DetResizeForTest': {
'image_shape': [640, 640]
}
}
self.preprocess_op = create_operators(pre_process_list) self.preprocess_op = create_operators(pre_process_list)
self.postprocess_op = build_post_process(postprocess_params) self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors, self.config = utility.create_predictor( self.predictor, self.input_tensor, self.output_tensors, self.config = utility.create_predictor(
...@@ -198,15 +204,19 @@ class TextDetector(object): ...@@ -198,15 +204,19 @@ class TextDetector(object):
if self.args.benchmark: if self.args.benchmark:
self.autolog.times.stamp() self.autolog.times.stamp()
if self.use_onnx:
self.input_tensor.copy_from_cpu(img) input_dict = {}
self.predictor.run() input_dict[self.input_tensor.name] = img
outputs = [] outputs = self.predictor.run(self.output_tensors, input_dict)
for output_tensor in self.output_tensors: else:
output = output_tensor.copy_to_cpu() self.input_tensor.copy_from_cpu(img)
outputs.append(output) self.predictor.run()
if self.args.benchmark: outputs = []
self.autolog.times.stamp() for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
if self.args.benchmark:
self.autolog.times.stamp()
preds = {} preds = {}
if self.det_algorithm == "EAST": if self.det_algorithm == "EAST":
......
tools/infer/predict_e2e.py
View file @ 03bb378f
...@@ -38,6 +38,7 @@ class TextE2E(object): ...@@ -38,6 +38,7 @@ class TextE2E(object):
def __init__(self, args): def __init__(self, args):
self.args = args self.args = args
self.e2e_algorithm = args.e2e_algorithm self.e2e_algorithm = args.e2e_algorithm
self.use_onnx = args.use_onnx
pre_process_list = [{ pre_process_list = [{
'E2EResizeForTest': {} 'E2EResizeForTest': {}
}, { }, {
...@@ -67,7 +68,6 @@ class TextE2E(object): ...@@ -67,7 +68,6 @@ class TextE2E(object):
postprocess_params["character_dict_path"] = args.e2e_char_dict_path postprocess_params["character_dict_path"] = args.e2e_char_dict_path
postprocess_params["valid_set"] = args.e2e_pgnet_valid_set postprocess_params["valid_set"] = args.e2e_pgnet_valid_set
postprocess_params["mode"] = args.e2e_pgnet_mode postprocess_params["mode"] = args.e2e_pgnet_mode
self.e2e_pgnet_polygon = args.e2e_pgnet_polygon
else: else:
logger.info("unknown e2e_algorithm:{}".format(self.e2e_algorithm)) logger.info("unknown e2e_algorithm:{}".format(self.e2e_algorithm))
sys.exit(0) sys.exit(0)
...@@ -106,21 +106,31 @@ class TextE2E(object): ...@@ -106,21 +106,31 @@ class TextE2E(object):
img = img.copy() img = img.copy()
starttime = time.time() starttime = time.time()
self.input_tensor.copy_from_cpu(img) if self.use_onnx:
self.predictor.run() input_dict = {}
outputs = [] input_dict[self.input_tensor.name] = img
for output_tensor in self.output_tensors: outputs = self.predictor.run(self.output_tensors, input_dict)
output = output_tensor.copy_to_cpu() preds = {}
outputs.append(output)
preds = {}
if self.e2e_algorithm == 'PGNet':
preds['f_border'] = outputs[0] preds['f_border'] = outputs[0]
preds['f_char'] = outputs[1] preds['f_char'] = outputs[1]
preds['f_direction'] = outputs[2] preds['f_direction'] = outputs[2]
preds['f_score'] = outputs[3] preds['f_score'] = outputs[3]
else: else:
raise NotImplementedError self.input_tensor.copy_from_cpu(img)
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
preds = {}
if self.e2e_algorithm == 'PGNet':
preds['f_border'] = outputs[0]
preds['f_char'] = outputs[1]
preds['f_direction'] = outputs[2]
preds['f_score'] = outputs[3]
else:
raise NotImplementedError
post_result = self.postprocess_op(preds, shape_list) post_result = self.postprocess_op(preds, shape_list)
points, strs = post_result['points'], post_result['texts'] points, strs = post_result['points'], post_result['texts']
dt_boxes = self.filter_tag_det_res_only_clip(points, ori_im.shape) dt_boxes = self.filter_tag_det_res_only_clip(points, ori_im.shape)
......
tools/infer/predict_rec.py
View file @ 03bb378f
...@@ -73,6 +73,7 @@ class TextRecognizer(object): ...@@ -73,6 +73,7 @@ class TextRecognizer(object):
self.predictor, self.input_tensor, self.output_tensors, self.config = \ self.predictor, self.input_tensor, self.output_tensors, self.config = \
utility.create_predictor(args, 'rec', logger) utility.create_predictor(args, 'rec', logger)
self.benchmark = args.benchmark self.benchmark = args.benchmark
self.use_onnx = args.use_onnx
if args.benchmark: if args.benchmark:
import auto_log import auto_log
pid = os.getpid() pid = os.getpid()
...@@ -106,8 +107,9 @@ class TextRecognizer(object): ...@@ -106,8 +107,9 @@ class TextRecognizer(object):
return norm_img.astype(np.float32) / 128. - 1. return norm_img.astype(np.float32) / 128. - 1.
assert imgC == img.shape[2] assert imgC == img.shape[2]
max_wh_ratio = max(max_wh_ratio, imgW / imgH)
imgW = int((32 * max_wh_ratio)) imgW = int((32 * max_wh_ratio))
if self.use_onnx:
imgW = 100
h, w = img.shape[:2] h, w = img.shape[:2]
ratio = w / float(h) ratio = w / float(h)
if math.ceil(imgH * ratio) > imgW: if math.ceil(imgH * ratio) > imgW:
...@@ -297,51 +299,72 @@ class TextRecognizer(object): ...@@ -297,51 +299,72 @@ class TextRecognizer(object):
gsrm_slf_attn_bias1_list, gsrm_slf_attn_bias1_list,
gsrm_slf_attn_bias2_list, gsrm_slf_attn_bias2_list,
] ]
input_names = self.predictor.get_input_names() if self.use_onnx:
for i in range(len(input_names)): input_dict = {}
input_tensor = self.predictor.get_input_handle(input_names[ input_dict[self.input_tensor.name] = norm_img_batch
i]) outputs = self.predictor.run(self.output_tensors,
input_tensor.copy_from_cpu(inputs[i]) input_dict)
self.predictor.run() preds = {"predict": outputs[2]}
outputs = [] else:
for output_tensor in self.output_tensors: input_names = self.predictor.get_input_names()
output = output_tensor.copy_to_cpu() for i in range(len(input_names)):
outputs.append(output) input_tensor = self.predictor.get_input_handle(
if self.benchmark: input_names[i])
self.autolog.times.stamp() input_tensor.copy_from_cpu(inputs[i])
preds = {"predict": outputs[2]} self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
if self.benchmark:
self.autolog.times.stamp()
preds = {"predict": outputs[2]}
elif self.rec_algorithm == "SAR": elif self.rec_algorithm == "SAR":
valid_ratios = np.concatenate(valid_ratios) valid_ratios = np.concatenate(valid_ratios)
inputs = [ inputs = [
norm_img_batch, norm_img_batch,
valid_ratios, valid_ratios,
] ]
input_names = self.predictor.get_input_names() if self.use_onnx:
for i in range(len(input_names)): input_dict = {}
input_tensor = self.predictor.get_input_handle(input_names[ input_dict[self.input_tensor.name] = norm_img_batch
i]) outputs = self.predictor.run(self.output_tensors,
input_tensor.copy_from_cpu(inputs[i]) input_dict)
self.predictor.run() preds = outputs[0]
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
if self.benchmark:
self.autolog.times.stamp()
preds = outputs[0]
else:
self.input_tensor.copy_from_cpu(norm_img_batch)
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
if self.benchmark:
self.autolog.times.stamp()
if len(outputs) != 1:
preds = outputs
else: else:
input_names = self.predictor.get_input_names()
for i in range(len(input_names)):
input_tensor = self.predictor.get_input_handle(
input_names[i])
input_tensor.copy_from_cpu(inputs[i])
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
if self.benchmark:
self.autolog.times.stamp()
preds = outputs[0] preds = outputs[0]
else:
if self.use_onnx:
input_dict = {}
input_dict[self.input_tensor.name] = norm_img_batch
outputs = self.predictor.run(self.output_tensors,
input_dict)
preds = outputs[0]
else:
self.input_tensor.copy_from_cpu(norm_img_batch)
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
if self.benchmark:
self.autolog.times.stamp()
if len(outputs) != 1:
preds = outputs
else:
preds = outputs[0]
rec_result = self.postprocess_op(preds) rec_result = self.postprocess_op(preds)
for rno in range(len(rec_result)): for rno in range(len(rec_result)):
rec_res[indices[beg_img_no + rno]] = rec_result[rno] rec_res[indices[beg_img_no + rno]] = rec_result[rno]
......
tools/infer/predict_system.py
View file @ 03bb378f
...@@ -49,11 +49,19 @@ class TextSystem(object): ...@@ -49,11 +49,19 @@ class TextSystem(object):
if self.use_angle_cls: if self.use_angle_cls:
self.text_classifier = predict_cls.TextClassifier(args) self.text_classifier = predict_cls.TextClassifier(args)
def print_draw_crop_rec_res(self, img_crop_list, rec_res): self.args = args
self.crop_image_res_index = 0
def draw_crop_rec_res(self, output_dir, img_crop_list, rec_res):
os.makedirs(output_dir, exist_ok=True)
bbox_num = len(img_crop_list) bbox_num = len(img_crop_list)
for bno in range(bbox_num): for bno in range(bbox_num):
cv2.imwrite("./output/img_crop_%d.jpg" % bno, img_crop_list[bno]) cv2.imwrite(
logger.info(bno, rec_res[bno]) os.path.join(output_dir,
f"mg_crop_{bno+self.crop_image_res_index}.jpg"),
img_crop_list[bno])
logger.debug(f"{bno}, {rec_res[bno]}")
self.crop_image_res_index += bbox_num
def __call__(self, img, cls=True): def __call__(self, img, cls=True):
ori_im = img.copy() ori_im = img.copy()
...@@ -80,7 +88,9 @@ class TextSystem(object): ...@@ -80,7 +88,9 @@ class TextSystem(object):
rec_res, elapse = self.text_recognizer(img_crop_list) rec_res, elapse = self.text_recognizer(img_crop_list)
logger.debug("rec_res num : {}, elapse : {}".format( logger.debug("rec_res num : {}, elapse : {}".format(
len(rec_res), elapse)) len(rec_res), elapse))
# self.print_draw_crop_rec_res(img_crop_list, rec_res) if self.args.save_crop_res:
self.draw_crop_rec_res(self.args.crop_res_save_dir, img_crop_list,
rec_res)
filter_boxes, filter_rec_res = [], [] filter_boxes, filter_rec_res = [], []
for box, rec_reuslt in zip(dt_boxes, rec_res): for box, rec_reuslt in zip(dt_boxes, rec_res):
text, score = rec_reuslt text, score = rec_reuslt
...@@ -135,17 +145,17 @@ def main(args): ...@@ -135,17 +145,17 @@ def main(args):
if not flag: if not flag:
img = cv2.imread(image_file) img = cv2.imread(image_file)
if img is None: if img is None:
logger.info("error in loading image:{}".format(image_file)) logger.debug("error in loading image:{}".format(image_file))
continue continue
starttime = time.time() starttime = time.time()
dt_boxes, rec_res = text_sys(img) dt_boxes, rec_res = text_sys(img)
elapse = time.time() - starttime elapse = time.time() - starttime
total_time += elapse total_time += elapse
logger.info( logger.debug(
str(idx) + " Predict time of %s: %.3fs" % (image_file, elapse)) str(idx) + " Predict time of %s: %.3fs" % (image_file, elapse))
for text, score in rec_res: for text, score in rec_res:
logger.info("{}, {:.3f}".format(text, score)) logger.debug("{}, {:.3f}".format(text, score))
if is_visualize: if is_visualize:
image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
...@@ -160,19 +170,17 @@ def main(args): ...@@ -160,19 +170,17 @@ def main(args):
scores, scores,
drop_score=drop_score, drop_score=drop_score,
font_path=font_path) font_path=font_path)
draw_img_save = "./inference_results/" draw_img_save_dir = args.draw_img_save_dir
if not os.path.exists(draw_img_save): os.makedirs(draw_img_save_dir, exist_ok=True)
os.makedirs(draw_img_save)
if flag: if flag:
image_file = image_file[:-3] + "png" image_file = image_file[:-3] + "png"
cv2.imwrite( cv2.imwrite(
os.path.join(draw_img_save, os.path.basename(image_file)), os.path.join(draw_img_save_dir, os.path.basename(image_file)),
draw_img[:, :, ::-1]) draw_img[:, :, ::-1])
logger.info("The visualized image saved in {}".format( logger.debug("The visualized image saved in {}".format(
os.path.join(draw_img_save, os.path.basename(image_file)))) os.path.join(draw_img_save_dir, os.path.basename(image_file))))
logger.info("The predict total time is {}".format(time.time() - _st)) logger.info("The predict total time is {}".format(time.time() - _st))
logger.info("\nThe predict total time is {}".format(total_time))
if args.benchmark: if args.benchmark:
text_sys.text_detector.autolog.report() text_sys.text_detector.autolog.report()
text_sys.text_recognizer.autolog.report() text_sys.text_recognizer.autolog.report()
......
tools/infer/utility.py
View file @ 03bb378f
...@@ -17,7 +17,7 @@ import os ...@@ -17,7 +17,7 @@ import os
import sys import sys
import cv2 import cv2
import numpy as np import numpy as np
import json import paddle
from PIL import Image, ImageDraw, ImageFont from PIL import Image, ImageDraw, ImageFont
import math import math
from paddle import inference from paddle import inference
...@@ -96,7 +96,6 @@ def init_args(): ...@@ -96,7 +96,6 @@ def init_args():
parser.add_argument( parser.add_argument(
"--e2e_char_dict_path", type=str, default="./ppocr/utils/ic15_dict.txt") "--e2e_char_dict_path", type=str, default="./ppocr/utils/ic15_dict.txt")
parser.add_argument("--e2e_pgnet_valid_set", type=str, default='totaltext') parser.add_argument("--e2e_pgnet_valid_set", type=str, default='totaltext')
parser.add_argument("--e2e_pgnet_polygon", type=str2bool, default=True)
parser.add_argument("--e2e_pgnet_mode", type=str, default='fast') parser.add_argument("--e2e_pgnet_mode", type=str, default='fast')
# params for text classifier # params for text classifier
...@@ -110,7 +109,13 @@ def init_args(): ...@@ -110,7 +109,13 @@ def init_args():
parser.add_argument("--enable_mkldnn", type=str2bool, default=False) parser.add_argument("--enable_mkldnn", type=str2bool, default=False)
parser.add_argument("--cpu_threads", type=int, default=10) parser.add_argument("--cpu_threads", type=int, default=10)
parser.add_argument("--use_pdserving", type=str2bool, default=False) parser.add_argument("--use_pdserving", type=str2bool, default=False)
parser.add_argument("--warmup", type=str2bool, default=True) parser.add_argument("--warmup", type=str2bool, default=False)
#
parser.add_argument(
"--draw_img_save_dir", type=str, default="./inference_results")
parser.add_argument("--save_crop_res", type=str2bool, default=False)
parser.add_argument("--crop_res_save_dir", type=str, default="./output")
# multi-process # multi-process
parser.add_argument("--use_mp", type=str2bool, default=False) parser.add_argument("--use_mp", type=str2bool, default=False)
...@@ -121,6 +126,7 @@ def init_args(): ...@@ -121,6 +126,7 @@ def init_args():
parser.add_argument("--save_log_path", type=str, default="./log_output/") parser.add_argument("--save_log_path", type=str, default="./log_output/")
parser.add_argument("--show_log", type=str2bool, default=True) parser.add_argument("--show_log", type=str2bool, default=True)
parser.add_argument("--use_onnx", type=str2bool, default=False)
return parser return parser
...@@ -144,25 +150,14 @@ def create_predictor(args, mode, logger): ...@@ -144,25 +150,14 @@ def create_predictor(args, mode, logger):
if model_dir is None: if model_dir is None:
logger.info("not find {} model file path {}".format(mode, model_dir)) logger.info("not find {} model file path {}".format(mode, model_dir))
sys.exit(0) sys.exit(0)
model_file_path = model_dir + "/inference.pdmodel" if args.use_onnx:
params_file_path = model_dir + "/inference.pdiparams" import onnxruntime as ort
if not os.path.exists(model_file_path): model_file_path = model_dir
raise ValueError("not find model file path {}".format(model_file_path)) if not os.path.exists(model_file_path):
if not os.path.exists(params_file_path): raise ValueError("not find model file path {}".format(
raise ValueError("not find params file path {}".format( model_file_path))
params_file_path)) sess = ort.InferenceSession(model_file_path)
return sess, sess.get_inputs()[0], None, None
config = inference.Config(model_file_path, params_file_path)
if hasattr(args, 'precision'):
if args.precision == "fp16" and args.use_tensorrt:
precision = inference.PrecisionType.Half
elif args.precision == "int8":
precision = inference.PrecisionType.Int8
else:
precision = inference.PrecisionType.Float32
else:
precision = inference.PrecisionType.Float32
if args.use_gpu: if args.use_gpu:
gpu_id = get_infer_gpuid() gpu_id = get_infer_gpuid()
...@@ -250,47 +245,154 @@ def create_predictor(args, mode, logger): ...@@ -250,47 +245,154 @@ def create_predictor(args, mode, logger):
min_input_shape = {"x": [1, 3, 48, 10]} min_input_shape = {"x": [1, 3, 48, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 48, 2000]} max_input_shape = {"x": [args.rec_batch_num, 3, 48, 2000]}
opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]} opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]}
model_file_path = model_dir + "/inference.pdmodel"
params_file_path = model_dir + "/inference.pdiparams"
if not os.path.exists(model_file_path):
raise ValueError("not find model file path {}".format(
model_file_path))
if not os.path.exists(params_file_path):
raise ValueError("not find params file path {}".format(
params_file_path))
config = inference.Config(model_file_path, params_file_path)
if hasattr(args, 'precision'):
if args.precision == "fp16" and args.use_tensorrt:
precision = inference.PrecisionType.Half
elif args.precision == "int8":
precision = inference.PrecisionType.Int8
else:
precision = inference.PrecisionType.Float32
else: else:
min_input_shape = {"x": [1, 3, 10, 10]} precision = inference.PrecisionType.Float32
max_input_shape = {"x": [1, 3, 1000, 1000]}
opt_input_shape = {"x": [1, 3, 500, 500]} if args.use_gpu:
config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape, gpu_id = get_infer_gpuid()
opt_input_shape) if gpu_id is None:
logger.warning(
"GPU is not found in current device by nvidia-smi. Please check your device or ignore it if run on jeston."
)
config.enable_use_gpu(args.gpu_mem, 0)
if args.use_tensorrt:
config.enable_tensorrt_engine(
precision_mode=precision,
max_batch_size=args.max_batch_size,
min_subgraph_size=args.min_subgraph_size)
# skip the minmum trt subgraph
if mode == "det":
min_input_shape = {
"x": [1, 3, 50, 50],
"conv2d_92.tmp_0": [1, 120, 20, 20],
"conv2d_91.tmp_0": [1, 24, 10, 10],
"conv2d_59.tmp_0": [1, 96, 20, 20],
"nearest_interp_v2_1.tmp_0": [1, 256, 10, 10],
"nearest_interp_v2_2.tmp_0": [1, 256, 20, 20],
"conv2d_124.tmp_0": [1, 256, 20, 20],
"nearest_interp_v2_3.tmp_0": [1, 64, 20, 20],
"nearest_interp_v2_4.tmp_0": [1, 64, 20, 20],
"nearest_interp_v2_5.tmp_0": [1, 64, 20, 20],
"elementwise_add_7": [1, 56, 2, 2],
"nearest_interp_v2_0.tmp_0": [1, 256, 2, 2]
}
max_input_shape = {
"x": [1, 3, 1280, 1280],
"conv2d_92.tmp_0": [1, 120, 400, 400],
"conv2d_91.tmp_0": [1, 24, 200, 200],
"conv2d_59.tmp_0": [1, 96, 400, 400],
"nearest_interp_v2_1.tmp_0": [1, 256, 200, 200],
"conv2d_124.tmp_0": [1, 256, 400, 400],
"nearest_interp_v2_2.tmp_0": [1, 256, 400, 400],
"nearest_interp_v2_3.tmp_0": [1, 64, 400, 400],
"nearest_interp_v2_4.tmp_0": [1, 64, 400, 400],
"nearest_interp_v2_5.tmp_0": [1, 64, 400, 400],
"elementwise_add_7": [1, 56, 400, 400],
"nearest_interp_v2_0.tmp_0": [1, 256, 400, 400]
}
opt_input_shape = {
"x": [1, 3, 640, 640],
"conv2d_92.tmp_0": [1, 120, 160, 160],
"conv2d_91.tmp_0": [1, 24, 80, 80],
"conv2d_59.tmp_0": [1, 96, 160, 160],
"nearest_interp_v2_1.tmp_0": [1, 256, 80, 80],
"nearest_interp_v2_2.tmp_0": [1, 256, 160, 160],
"conv2d_124.tmp_0": [1, 256, 160, 160],
"nearest_interp_v2_3.tmp_0": [1, 64, 160, 160],
"nearest_interp_v2_4.tmp_0": [1, 64, 160, 160],
"nearest_interp_v2_5.tmp_0": [1, 64, 160, 160],
"elementwise_add_7": [1, 56, 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":
min_input_shape = {"x": [1, 3, 32, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 32, 1024]}
opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]}
elif mode == "cls":
min_input_shape = {"x": [1, 3, 48, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 48, 1024]}
opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]}
else:
min_input_shape = {"x": [1, 3, 10, 10]}
max_input_shape = {"x": [1, 3, 512, 512]}
opt_input_shape = {"x": [1, 3, 256, 256]}
config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
opt_input_shape)
else:
config.disable_gpu()
if hasattr(args, "cpu_threads"):
config.set_cpu_math_library_num_threads(args.cpu_threads)
else: else:
# default cpu threads as 10 config.disable_gpu()
config.set_cpu_math_library_num_threads(10) if hasattr(args, "cpu_threads"):
if args.enable_mkldnn: config.set_cpu_math_library_num_threads(args.cpu_threads)
# cache 10 different shapes for mkldnn to avoid memory leak else:
config.set_mkldnn_cache_capacity(10) # default cpu threads as 10
config.enable_mkldnn() config.set_cpu_math_library_num_threads(10)
if args.precision == "fp16": if args.enable_mkldnn:
config.enable_mkldnn_bfloat16() # cache 10 different shapes for mkldnn to avoid memory leak
# enable memory optim config.set_mkldnn_cache_capacity(10)
config.enable_memory_optim() config.enable_mkldnn()
config.disable_glog_info() if args.precision == "fp16":
config.enable_mkldnn_bfloat16()
config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass") # enable memory optim
if mode == 'table': config.enable_memory_optim()
config.delete_pass("fc_fuse_pass") # not supported for table config.disable_glog_info()
config.switch_use_feed_fetch_ops(False)
config.switch_ir_optim(True) config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
if mode == 'table':
# create predictor config.delete_pass("fc_fuse_pass") # not supported for table
predictor = inference.create_predictor(config) config.switch_use_feed_fetch_ops(False)
input_names = predictor.get_input_names() config.switch_ir_optim(True)
for name in input_names:
input_tensor = predictor.get_input_handle(name) # create predictor
output_names = predictor.get_output_names() predictor = inference.create_predictor(config)
output_tensors = [] input_names = predictor.get_input_names()
for output_name in output_names: for name in input_names:
output_tensor = predictor.get_output_handle(output_name) input_tensor = predictor.get_input_handle(name)
output_tensors.append(output_tensor) output_names = predictor.get_output_names()
return predictor, input_tensor, output_tensors, config output_tensors = []
for output_name in output_names:
output_tensor = predictor.get_output_handle(output_name)
output_tensors.append(output_tensor)
return predictor, input_tensor, output_tensors, config
def get_infer_gpuid(): def get_infer_gpuid():
...@@ -593,5 +695,12 @@ def get_rotate_crop_image(img, points): ...@@ -593,5 +695,12 @@ def get_rotate_crop_image(img, points):
return dst_img return dst_img
def check_gpu(use_gpu):
if use_gpu and not paddle.is_compiled_with_cuda():
use_gpu = False
return use_gpu
if __name__ == '__main__': if __name__ == '__main__':
pass pass
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