split python and serving
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MissPenguin
parent
8e5bfa3fb5
commit
23dc0a3d90
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@ -33,8 +33,7 @@ cpp_infer_key1=$(func_parser_key "${lines[13]}")
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cpp_infer_value1=$(func_parser_value "${lines[13]}")
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cpp_benchmark_key=$(func_parser_key "${lines[14]}")
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cpp_benchmark_value=$(func_parser_value "${lines[14]}")
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echo $use_opencv
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echo $cpp_infer_img_dir
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LOG_PATH="./tests/output"
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mkdir -p ${LOG_PATH}
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@ -0,0 +1,173 @@
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#!/bin/bash
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source tests/common_func.sh
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FILENAME=$1
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dataline=$(awk 'NR==1, NR==51{print}' $FILENAME)
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# parser params
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IFS=$'\n'
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lines=(${dataline})
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# The training params
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model_name=$(func_parser_value "${lines[1]}")
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python=$(func_parser_value "${lines[2]}")
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gpu_list=$(func_parser_value "${lines[3]}")
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train_use_gpu_key=$(func_parser_key "${lines[4]}")
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train_use_gpu_value=$(func_parser_value "${lines[4]}")
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autocast_list=$(func_parser_value "${lines[5]}")
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autocast_key=$(func_parser_key "${lines[5]}")
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epoch_key=$(func_parser_key "${lines[6]}")
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epoch_num=$(func_parser_params "${lines[6]}")
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save_model_key=$(func_parser_key "${lines[7]}")
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train_batch_key=$(func_parser_key "${lines[8]}")
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train_batch_value=$(func_parser_params "${lines[8]}")
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pretrain_model_key=$(func_parser_key "${lines[9]}")
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pretrain_model_value=$(func_parser_value "${lines[9]}")
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train_model_name=$(func_parser_value "${lines[10]}")
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train_infer_img_dir=$(func_parser_value "${lines[11]}")
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train_param_key1=$(func_parser_key "${lines[12]}")
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train_param_value1=$(func_parser_value "${lines[12]}")
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trainer_list=$(func_parser_value "${lines[14]}")
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trainer_norm=$(func_parser_key "${lines[15]}")
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norm_trainer=$(func_parser_value "${lines[15]}")
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pact_key=$(func_parser_key "${lines[16]}")
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pact_trainer=$(func_parser_value "${lines[16]}")
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fpgm_key=$(func_parser_key "${lines[17]}")
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fpgm_trainer=$(func_parser_value "${lines[17]}")
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distill_key=$(func_parser_key "${lines[18]}")
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distill_trainer=$(func_parser_value "${lines[18]}")
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trainer_key1=$(func_parser_key "${lines[19]}")
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trainer_value1=$(func_parser_value "${lines[19]}")
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trainer_key2=$(func_parser_key "${lines[20]}")
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trainer_value2=$(func_parser_value "${lines[20]}")
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eval_py=$(func_parser_value "${lines[23]}")
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eval_key1=$(func_parser_key "${lines[24]}")
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eval_value1=$(func_parser_value "${lines[24]}")
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save_infer_key=$(func_parser_key "${lines[27]}")
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export_weight=$(func_parser_key "${lines[28]}")
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norm_export=$(func_parser_value "${lines[29]}")
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pact_export=$(func_parser_value "${lines[30]}")
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fpgm_export=$(func_parser_value "${lines[31]}")
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distill_export=$(func_parser_value "${lines[32]}")
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export_key1=$(func_parser_key "${lines[33]}")
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export_value1=$(func_parser_value "${lines[33]}")
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export_key2=$(func_parser_key "${lines[34]}")
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export_value2=$(func_parser_value "${lines[34]}")
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# parser inference model
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infer_model_dir_list=$(func_parser_value "${lines[36]}")
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infer_export_list=$(func_parser_value "${lines[37]}")
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infer_is_quant=$(func_parser_value "${lines[38]}")
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# parser inference
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inference_py=$(func_parser_value "${lines[39]}")
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use_gpu_key=$(func_parser_key "${lines[40]}")
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use_gpu_list=$(func_parser_value "${lines[40]}")
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use_mkldnn_key=$(func_parser_key "${lines[41]}")
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use_mkldnn_list=$(func_parser_value "${lines[41]}")
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cpu_threads_key=$(func_parser_key "${lines[42]}")
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cpu_threads_list=$(func_parser_value "${lines[42]}")
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batch_size_key=$(func_parser_key "${lines[43]}")
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batch_size_list=$(func_parser_value "${lines[43]}")
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use_trt_key=$(func_parser_key "${lines[44]}")
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use_trt_list=$(func_parser_value "${lines[44]}")
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precision_key=$(func_parser_key "${lines[45]}")
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precision_list=$(func_parser_value "${lines[45]}")
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infer_model_key=$(func_parser_key "${lines[46]}")
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image_dir_key=$(func_parser_key "${lines[47]}")
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infer_img_dir=$(func_parser_value "${lines[47]}")
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save_log_key=$(func_parser_key "${lines[48]}")
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benchmark_key=$(func_parser_key "${lines[49]}")
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benchmark_value=$(func_parser_value "${lines[49]}")
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infer_key1=$(func_parser_key "${lines[50]}")
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infer_value1=$(func_parser_value "${lines[50]}")
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LOG_PATH="./tests/output"
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mkdir -p ${LOG_PATH}
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status_log="${LOG_PATH}/results_python.log"
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function func_inference(){
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IFS='|'
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_python=$1
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_script=$2
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_model_dir=$3
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_log_path=$4
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_img_dir=$5
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_flag_quant=$6
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# inference
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for use_gpu in ${use_gpu_list[*]}; do
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if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
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for use_mkldnn in ${use_mkldnn_list[*]}; do
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if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
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continue
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fi
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for threads in ${cpu_threads_list[*]}; do
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for batch_size in ${batch_size_list[*]}; do
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_save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log"
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set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
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set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
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set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
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set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}")
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set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
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set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
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command="${_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 "
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eval $command
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last_status=${PIPESTATUS[0]}
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eval "cat ${_save_log_path}"
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status_check $last_status "${command}" "${status_log}"
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done
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done
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done
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elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
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for use_trt in ${use_trt_list[*]}; do
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for precision in ${precision_list[*]}; do
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if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
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continue
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fi
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if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
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continue
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fi
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if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then
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continue
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fi
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for batch_size in ${batch_size_list[*]}; do
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_save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
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set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
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set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
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set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
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set_tensorrt=$(func_set_params "${use_trt_key}" "${use_trt}")
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set_precision=$(func_set_params "${precision_key}" "${precision}")
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set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
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set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
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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 "
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eval $command
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last_status=${PIPESTATUS[0]}
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eval "cat ${_save_log_path}"
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status_check $last_status "${command}" "${status_log}"
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done
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done
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done
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else
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echo "Does not support hardware other than CPU and GPU Currently!"
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fi
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done
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}
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# set cuda device
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GPUID=$2
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if [ ${#GPUID} -le 0 ];then
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env=" "
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else
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env="export CUDA_VISIBLE_DEVICES=${GPUID}"
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fi
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set CUDA_VISIBLE_DEVICES
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eval $env
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echo "################### run test ###################"
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@ -0,0 +1,131 @@
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#!/bin/bash
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source tests/common_func.sh
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FILENAME=$1
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dataline=$(awk 'NR==67, NR==81{print}' $FILENAME)
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# parser params
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IFS=$'\n'
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lines=(${dataline})
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# parser serving
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trans_model_py=$(func_parser_value "${lines[1]}")
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infer_model_dir_key=$(func_parser_key "${lines[2]}")
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infer_model_dir_value=$(func_parser_value "${lines[2]}")
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model_filename_key=$(func_parser_key "${lines[3]}")
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model_filename_value=$(func_parser_value "${lines[3]}")
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params_filename_key=$(func_parser_key "${lines[4]}")
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params_filename_value=$(func_parser_value "${lines[4]}")
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serving_server_key=$(func_parser_key "${lines[5]}")
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serving_server_value=$(func_parser_value "${lines[5]}")
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serving_client_key=$(func_parser_key "${lines[6]}")
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serving_client_value=$(func_parser_value "${lines[6]}")
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serving_dir_value=$(func_parser_value "${lines[7]}")
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web_service_py=$(func_parser_value "${lines[8]}")
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web_use_gpu_key=$(func_parser_key "${lines[9]}")
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web_use_gpu_list=$(func_parser_value "${lines[9]}")
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web_use_mkldnn_key=$(func_parser_key "${lines[10]}")
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web_use_mkldnn_list=$(func_parser_value "${lines[10]}")
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web_cpu_threads_key=$(func_parser_key "${lines[11]}")
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web_cpu_threads_list=$(func_parser_value "${lines[11]}")
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web_use_trt_key=$(func_parser_key "${lines[12]}")
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web_use_trt_list=$(func_parser_value "${lines[12]}")
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web_precision_key=$(func_parser_key "${lines[13]}")
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web_precision_list=$(func_parser_value "${lines[13]}")
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pipeline_py=$(func_parser_value "${lines[14]}")
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LOG_PATH="./tests/output"
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mkdir -p ${LOG_PATH}
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status_log="${LOG_PATH}/results_serving.log"
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function func_serving(){
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IFS='|'
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_python=$1
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_script=$2
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_model_dir=$3
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# pdserving
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set_dirname=$(func_set_params "${infer_model_dir_key}" "${infer_model_dir_value}")
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set_model_filename=$(func_set_params "${model_filename_key}" "${model_filename_value}")
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set_params_filename=$(func_set_params "${params_filename_key}" "${params_filename_value}")
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set_serving_server=$(func_set_params "${serving_server_key}" "${serving_server_value}")
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set_serving_client=$(func_set_params "${serving_client_key}" "${serving_client_value}")
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trans_model_cmd="${python} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
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eval $trans_model_cmd
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cd ${serving_dir_value}
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echo $PWD
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unset https_proxy
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unset http_proxy
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for use_gpu in ${web_use_gpu_list[*]}; do
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echo ${ues_gpu}
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if [ ${use_gpu} = "null" ]; then
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for use_mkldnn in ${web_use_mkldnn_list[*]}; do
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if [ ${use_mkldnn} = "False" ]; then
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continue
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fi
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for threads in ${web_cpu_threads_list[*]}; do
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_save_log_path="${_log_path}/server_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_1.log"
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set_cpu_threads=$(func_set_params "${web_cpu_threads_key}" "${threads}")
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web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}=${use_gpu} ${web_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} &>${_save_log_path} &"
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eval $web_service_cmd
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sleep 2s
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pipeline_cmd="${python} ${pipeline_py}"
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eval $pipeline_cmd
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last_status=${PIPESTATUS[0]}
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eval "cat ${_save_log_path}"
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status_check $last_status "${pipeline_cmd}" "${status_log}"
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PID=$!
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kill $PID
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sleep 2s
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ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
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done
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done
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elif [ ${use_gpu} = "0" ]; then
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for use_trt in ${web_use_trt_list[*]}; do
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for precision in ${web_precision_list[*]}; do
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if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
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continue
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fi
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if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
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continue
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fi
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if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [[ ${_flag_quant} = "True" ]]; then
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continue
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fi
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_save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_1.log"
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set_tensorrt=$(func_set_params "${web_use_trt_key}" "${use_trt}")
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set_precision=$(func_set_params "${web_precision_key}" "${precision}")
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web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} &>${_save_log_path} & "
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eval $web_service_cmd
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sleep 2s
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pipeline_cmd="${python} ${pipeline_py}"
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eval $pipeline_cmd
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last_status=${PIPESTATUS[0]}
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eval "cat ${_save_log_path}"
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status_check $last_status "${pipeline_cmd}" "${status_log}"
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PID=$!
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kill $PID
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sleep 2s
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ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
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done
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done
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else
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echo "Does not support hardware other than CPU and GPU Currently!"
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fi
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done
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}
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# set cuda device
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GPUID=$2
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if [ ${#GPUID} -le 0 ];then
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env=" "
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else
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env="export CUDA_VISIBLE_DEVICES=${GPUID}"
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fi
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set CUDA_VISIBLE_DEVICES
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eval $env
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echo "################### run test ###################"
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