Merge pull request #4334 from LDOUBLEV/test_v10

[full_chain] slice test.sh to test_python.sh

tests/test.sh

-#!/bin/bash
-FILENAME=$1
-# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer', 'cpp_infer', 'serving_infer', 'klquant_infer']
-MODE=$2
-if [ ${MODE} = "cpp_infer" ]; then
-    dataline=$(awk 'NR==67, NR==81{print}'  $FILENAME)
-elif [ ${MODE} = "serving_infer" ]; then
-    dataline=$(awk 'NR==52, NR==66{print}'  $FILENAME)
-elif [ ${MODE} = "klquant_infer" ]; then
-    dataline=$(awk 'NR==82, NR==98{print}'  $FILENAME)
-else
-    dataline=$(awk 'NR==1, NR==51{print}'  $FILENAME)
-fi
-
-# parser params
-IFS=$'\n'
-lines=(${dataline})
-
-function func_parser_key(){
-    strs=$1
-    IFS=":"
-    array=(${strs})
-    tmp=${array[0]}
-    echo ${tmp}
-}
-function func_parser_value(){
-    strs=$1
-    IFS=":"
-    array=(${strs})
-    tmp=${array[1]}
-    echo ${tmp}
-}
-function func_set_params(){
-    key=$1
-    value=$2
-    if [ ${key} = "null" ];then
-        echo " "
-    elif [[ ${value} = "null" ]] || [[ ${value} = " " ]] || [ ${#value} -le 0 ];then
-        echo " "
-    else 
-        echo "${key}=${value}"
-    fi
-}
-function func_parser_params(){
-    strs=$1
-    IFS=":"
-    array=(${strs})
-    key=${array[0]}
-    tmp=${array[1]}
-    IFS="|"
-    res=""
-    for _params in ${tmp[*]}; do
-        IFS="="
-        array=(${_params})
-        mode=${array[0]}
-        value=${array[1]}
-        if [[ ${mode} = ${MODE} ]]; then
-            IFS="|"
-            #echo $(func_set_params "${mode}" "${value}")
-            echo $value
-            break
-        fi
-        IFS="|"
-    done
-    echo ${res}
-}
-function status_check(){
-    last_status=$1   # the exit code
-    run_command=$2
-    run_log=$3
-    if [ $last_status -eq 0 ]; then
-        echo -e "\033[33m Run successfully with command - ${run_command}!  \033[0m" | tee -a ${run_log}
-    else
-        echo -e "\033[33m Run failed with command - ${run_command}!  \033[0m" | tee -a ${run_log}
-    fi
-}
-
-IFS=$'\n'
-# The training params
-model_name=$(func_parser_value "${lines[1]}")
-python=$(func_parser_value "${lines[2]}")
-gpu_list=$(func_parser_value "${lines[3]}")
-train_use_gpu_key=$(func_parser_key "${lines[4]}")
-train_use_gpu_value=$(func_parser_value "${lines[4]}")
-autocast_list=$(func_parser_value "${lines[5]}")
-autocast_key=$(func_parser_key "${lines[5]}")
-epoch_key=$(func_parser_key "${lines[6]}")
-epoch_num=$(func_parser_params "${lines[6]}")
-save_model_key=$(func_parser_key "${lines[7]}")
-train_batch_key=$(func_parser_key "${lines[8]}")
-train_batch_value=$(func_parser_params "${lines[8]}")
-pretrain_model_key=$(func_parser_key "${lines[9]}")
-pretrain_model_value=$(func_parser_value "${lines[9]}")
-train_model_name=$(func_parser_value "${lines[10]}")
-train_infer_img_dir=$(func_parser_value "${lines[11]}")
-train_param_key1=$(func_parser_key "${lines[12]}")
-train_param_value1=$(func_parser_value "${lines[12]}")
-
-trainer_list=$(func_parser_value "${lines[14]}")
-trainer_norm=$(func_parser_key "${lines[15]}")
-norm_trainer=$(func_parser_value "${lines[15]}")
-pact_key=$(func_parser_key "${lines[16]}")
-pact_trainer=$(func_parser_value "${lines[16]}")
-fpgm_key=$(func_parser_key "${lines[17]}")
-fpgm_trainer=$(func_parser_value "${lines[17]}")
-distill_key=$(func_parser_key "${lines[18]}")
-distill_trainer=$(func_parser_value "${lines[18]}")
-trainer_key1=$(func_parser_key "${lines[19]}")
-trainer_value1=$(func_parser_value "${lines[19]}")
-trainer_key2=$(func_parser_key "${lines[20]}")
-trainer_value2=$(func_parser_value "${lines[20]}")
-
-eval_py=$(func_parser_value "${lines[23]}")
-eval_key1=$(func_parser_key "${lines[24]}")
-eval_value1=$(func_parser_value "${lines[24]}")
-
-save_infer_key=$(func_parser_key "${lines[27]}")
-export_weight=$(func_parser_key "${lines[28]}")
-norm_export=$(func_parser_value "${lines[29]}")
-pact_export=$(func_parser_value "${lines[30]}")
-fpgm_export=$(func_parser_value "${lines[31]}")
-distill_export=$(func_parser_value "${lines[32]}")
-export_key1=$(func_parser_key "${lines[33]}")
-export_value1=$(func_parser_value "${lines[33]}")
-export_key2=$(func_parser_key "${lines[34]}")
-export_value2=$(func_parser_value "${lines[34]}")
-
-# parser inference model 
-infer_model_dir_list=$(func_parser_value "${lines[36]}")
-infer_export_list=$(func_parser_value "${lines[37]}")
-infer_is_quant=$(func_parser_value "${lines[38]}")
-# parser inference 
-inference_py=$(func_parser_value "${lines[39]}")
-use_gpu_key=$(func_parser_key "${lines[40]}")
-use_gpu_list=$(func_parser_value "${lines[40]}")
-use_mkldnn_key=$(func_parser_key "${lines[41]}")
-use_mkldnn_list=$(func_parser_value "${lines[41]}")
-cpu_threads_key=$(func_parser_key "${lines[42]}")
-cpu_threads_list=$(func_parser_value "${lines[42]}")
-batch_size_key=$(func_parser_key "${lines[43]}")
-batch_size_list=$(func_parser_value "${lines[43]}")
-use_trt_key=$(func_parser_key "${lines[44]}")
-use_trt_list=$(func_parser_value "${lines[44]}")
-precision_key=$(func_parser_key "${lines[45]}")
-precision_list=$(func_parser_value "${lines[45]}")
-infer_model_key=$(func_parser_key "${lines[46]}")
-image_dir_key=$(func_parser_key "${lines[47]}")
-infer_img_dir=$(func_parser_value "${lines[47]}")
-save_log_key=$(func_parser_key "${lines[48]}")
-benchmark_key=$(func_parser_key "${lines[49]}")
-benchmark_value=$(func_parser_value "${lines[49]}")
-infer_key1=$(func_parser_key "${lines[50]}")
-infer_value1=$(func_parser_value "${lines[50]}")
-
-# parser serving
-if [ ${MODE} = "klquant_infer" ]; then
-    # parser inference model 
-    infer_model_dir_list=$(func_parser_value "${lines[1]}")
-    infer_export_list=$(func_parser_value "${lines[2]}")
-    infer_is_quant=$(func_parser_value "${lines[3]}")
-    # parser inference 
-    inference_py=$(func_parser_value "${lines[4]}")
-    use_gpu_key=$(func_parser_key "${lines[5]}")
-    use_gpu_list=$(func_parser_value "${lines[5]}")
-    use_mkldnn_key=$(func_parser_key "${lines[6]}")
-    use_mkldnn_list=$(func_parser_value "${lines[6]}")
-    cpu_threads_key=$(func_parser_key "${lines[7]}")
-    cpu_threads_list=$(func_parser_value "${lines[7]}")
-    batch_size_key=$(func_parser_key "${lines[8]}")
-    batch_size_list=$(func_parser_value "${lines[8]}")
-    use_trt_key=$(func_parser_key "${lines[9]}")
-    use_trt_list=$(func_parser_value "${lines[9]}")
-    precision_key=$(func_parser_key "${lines[10]}")
-    precision_list=$(func_parser_value "${lines[10]}")
-    infer_model_key=$(func_parser_key "${lines[11]}")
-    image_dir_key=$(func_parser_key "${lines[12]}")
-    infer_img_dir=$(func_parser_value "${lines[12]}")
-    save_log_key=$(func_parser_key "${lines[13]}")
-    benchmark_key=$(func_parser_key "${lines[14]}")
-    benchmark_value=$(func_parser_value "${lines[14]}")
-    infer_key1=$(func_parser_key "${lines[15]}")
-    infer_value1=$(func_parser_value "${lines[15]}")
-fi
-# parser serving
-if [ ${MODE} = "server_infer" ]; then
-    trans_model_py=$(func_parser_value "${lines[1]}")
-    infer_model_dir_key=$(func_parser_key "${lines[2]}")
-    infer_model_dir_value=$(func_parser_value "${lines[2]}")
-    model_filename_key=$(func_parser_key "${lines[3]}")
-    model_filename_value=$(func_parser_value "${lines[3]}")
-    params_filename_key=$(func_parser_key "${lines[4]}")
-    params_filename_value=$(func_parser_value "${lines[4]}")
-    serving_server_key=$(func_parser_key "${lines[5]}")
-    serving_server_value=$(func_parser_value "${lines[5]}")
-    serving_client_key=$(func_parser_key "${lines[6]}")
-    serving_client_value=$(func_parser_value "${lines[6]}")
-    serving_dir_value=$(func_parser_value "${lines[7]}")
-    web_service_py=$(func_parser_value "${lines[8]}")
-    web_use_gpu_key=$(func_parser_key "${lines[9]}")
-    web_use_gpu_list=$(func_parser_value "${lines[9]}")
-    web_use_mkldnn_key=$(func_parser_key "${lines[10]}")
-    web_use_mkldnn_list=$(func_parser_value "${lines[10]}")
-    web_cpu_threads_key=$(func_parser_key "${lines[11]}")
-    web_cpu_threads_list=$(func_parser_value "${lines[11]}")
-    web_use_trt_key=$(func_parser_key "${lines[12]}")
-    web_use_trt_list=$(func_parser_value "${lines[12]}")
-    web_precision_key=$(func_parser_key "${lines[13]}")
-    web_precision_list=$(func_parser_value "${lines[13]}")
-    pipeline_py=$(func_parser_value "${lines[14]}")
-fi
-
-if [ ${MODE} = "cpp_infer" ]; then
-    # parser cpp inference model 
-    cpp_infer_model_dir_list=$(func_parser_value "${lines[1]}")
-    cpp_infer_is_quant=$(func_parser_value "${lines[2]}")
-    # parser cpp inference 
-    inference_cmd=$(func_parser_value "${lines[3]}")
-    cpp_use_gpu_key=$(func_parser_key "${lines[4]}")
-    cpp_use_gpu_list=$(func_parser_value "${lines[4]}")
-    cpp_use_mkldnn_key=$(func_parser_key "${lines[5]}")
-    cpp_use_mkldnn_list=$(func_parser_value "${lines[5]}")
-    cpp_cpu_threads_key=$(func_parser_key "${lines[6]}")
-    cpp_cpu_threads_list=$(func_parser_value "${lines[6]}")
-    cpp_batch_size_key=$(func_parser_key "${lines[7]}")
-    cpp_batch_size_list=$(func_parser_value "${lines[7]}")
-    cpp_use_trt_key=$(func_parser_key "${lines[8]}")
-    cpp_use_trt_list=$(func_parser_value "${lines[8]}")
-    cpp_precision_key=$(func_parser_key "${lines[9]}")
-    cpp_precision_list=$(func_parser_value "${lines[9]}")
-    cpp_infer_model_key=$(func_parser_key "${lines[10]}")
-    cpp_image_dir_key=$(func_parser_key "${lines[11]}")
-    cpp_infer_img_dir=$(func_parser_value "${lines[12]}")
-    cpp_infer_key1=$(func_parser_key "${lines[13]}")
-    cpp_infer_value1=$(func_parser_value "${lines[13]}")
-    cpp_benchmark_key=$(func_parser_key "${lines[14]}")
-    cpp_benchmark_value=$(func_parser_value "${lines[14]}")
-fi
-
-
-
-LOG_PATH="./tests/output"
-mkdir -p ${LOG_PATH}
-status_log="${LOG_PATH}/results.log"
-
-
-function func_inference(){
-    IFS='|'
-    _python=$1
-    _script=$2
-    _model_dir=$3
-    _log_path=$4
-    _img_dir=$5
-    _flag_quant=$6
-    # inference 
-    for use_gpu in ${use_gpu_list[*]}; do
-        if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
-            for use_mkldnn in ${use_mkldnn_list[*]}; do
-                if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
-                    continue
-                fi
-                for threads in ${cpu_threads_list[*]}; do
-                    for batch_size in ${batch_size_list[*]}; do
-                        _save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log"
-                        set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
-                        set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
-                        set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
-                        set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}")
-                        set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
-                        set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
-                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
-                        eval $command
-                        last_status=${PIPESTATUS[0]}
-                        eval "cat ${_save_log_path}"
-                        status_check $last_status "${command}" "${status_log}"
-                    done
-                done
-            done
-        elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
-            for use_trt in ${use_trt_list[*]}; do
-                for precision in ${precision_list[*]}; do
-                    if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
-                        continue
-                    fi 
-                    if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
-                        continue
-                    fi
-                    if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then
-                        continue
-                    fi
-                    for batch_size in ${batch_size_list[*]}; do
-                        _save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
-                        set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
-                        set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
-                        set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
-                        set_tensorrt=$(func_set_params "${use_trt_key}" "${use_trt}")
-                        set_precision=$(func_set_params "${precision_key}" "${precision}")
-                        set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
-                        set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
-                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
-                        eval $command
-                        last_status=${PIPESTATUS[0]}
-                        eval "cat ${_save_log_path}"
-                        status_check $last_status "${command}" "${status_log}"
-                        
-                    done
-                done
-            done
-        else
-            echo "Does not support hardware other than CPU and GPU Currently!"
-        fi
-    done
-}
-function func_serving(){
-    IFS='|'
-    _python=$1
-    _script=$2
-    _model_dir=$3
-    # pdserving
-    set_dirname=$(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_serving_server=$(func_set_params "${serving_server_key}" "${serving_server_value}")
-    set_serving_client=$(func_set_params "${serving_client_key}" "${serving_client_value}")
-    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
-    cd ${serving_dir_value}
-    echo $PWD
-    unset https_proxy
-    unset http_proxy
-    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 ${web_cpu_threads_list[*]}; do
-                      _save_log_path="${_log_path}/server_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} ${set_cpu_threads} &>${_save_log_path} &"
-                      eval $web_service_cmd
-                      sleep 2s
-                      pipeline_cmd="${python} ${pipeline_py}"
-                      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}/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} ${set_precision} &>${_save_log_path} & "
-                    eval $web_service_cmd
-                    sleep 2s
-                    pipeline_cmd="${python} ${pipeline_py}"
-                    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
-        else
-            echo "Does not support hardware other than CPU and GPU Currently!"
-        fi
-    done
-}
-
-function func_cpp_inference(){
-    IFS='|'
-    _script=$1
-    _model_dir=$2
-    _log_path=$3
-    _img_dir=$4
-    _flag_quant=$5
-    # inference 
-    for use_gpu in ${cpp_use_gpu_list[*]}; do
-        if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
-            for use_mkldnn in ${cpp_use_mkldnn_list[*]}; do
-                if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
-                    continue
-                fi
-                for threads in ${cpp_cpu_threads_list[*]}; do
-                    for batch_size in ${cpp_batch_size_list[*]}; do
-                        _save_log_path="${_log_path}/cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log"
-                        set_infer_data=$(func_set_params "${cpp_image_dir_key}" "${_img_dir}")
-                        set_benchmark=$(func_set_params "${cpp_benchmark_key}" "${cpp_benchmark_value}")
-                        set_batchsize=$(func_set_params "${cpp_batch_size_key}" "${batch_size}")
-                        set_cpu_threads=$(func_set_params "${cpp_cpu_threads_key}" "${threads}")
-                        set_model_dir=$(func_set_params "${cpp_infer_model_key}" "${_model_dir}")
-                        set_infer_params1=$(func_set_params "${cpp_infer_key1}" "${cpp_infer_value1}")
-                        command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${cpp_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
-                        eval $command
-                        last_status=${PIPESTATUS[0]}
-                        eval "cat ${_save_log_path}"
-                        status_check $last_status "${command}" "${status_log}"
-                    done
-                done
-            done
-        elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
-            for use_trt in ${cpp_use_trt_list[*]}; do
-                for precision in ${cpp_precision_list[*]}; do
-                    if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
-                        continue
-                    fi 
-                    if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
-                        continue
-                    fi
-                    if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then
-                        continue
-                    fi
-                    for batch_size in ${cpp_batch_size_list[*]}; do
-                        _save_log_path="${_log_path}/cpp_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
-                        set_infer_data=$(func_set_params "${cpp_image_dir_key}" "${_img_dir}")
-                        set_benchmark=$(func_set_params "${cpp_benchmark_key}" "${cpp_benchmark_value}")
-                        set_batchsize=$(func_set_params "${cpp_batch_size_key}" "${batch_size}")
-                        set_tensorrt=$(func_set_params "${cpp_use_trt_key}" "${use_trt}")
-                        set_precision=$(func_set_params "${cpp_precision_key}" "${precision}")
-                        set_model_dir=$(func_set_params "${cpp_infer_model_key}" "${_model_dir}")
-                        set_infer_params1=$(func_set_params "${cpp_infer_key1}" "${cpp_infer_value1}")
-                        command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
-                        eval $command
-                        last_status=${PIPESTATUS[0]}
-                        eval "cat ${_save_log_path}"
-                        status_check $last_status "${command}" "${status_log}"
-                        
-                    done
-                done
-            done
-        else
-            echo "Does not support hardware other than CPU and GPU Currently!"
-        fi
-    done
-}
-
-if [ ${MODE} = "infer" ] || [ ${MODE} = "klquant_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=${infer_quant_flag[Count]}
-        func_inference "${python}" "${inference_py}" "${save_infer_dir}" "${LOG_PATH}" "${infer_img_dir}" ${is_quant}
-        Count=$(($Count + 1))
-    done
-elif [ ${MODE} = "cpp_infer" ]; then
-    GPUID=$3
-    if [ ${#GPUID} -le 0 ];then
-        env=" "
-    else
-        env="export CUDA_VISIBLE_DEVICES=${GPUID}"
-    fi
-    # set CUDA_VISIBLE_DEVICES
-    eval $env
-    export Count=0
-    IFS="|"
-    infer_quant_flag=(${cpp_infer_is_quant})
-    for infer_model in ${cpp_infer_model_dir_list[*]}; do
-        #run inference
-        is_quant=${infer_quant_flag[Count]}
-        func_cpp_inference "${inference_cmd}" "${infer_model}" "${LOG_PATH}" "${cpp_infer_img_dir}" ${is_quant}
-        Count=$(($Count + 1))
-    done
-    
-elif [ ${MODE} = "serving_infer" ]; then
-    GPUID=$3
-    if [ ${#GPUID} -le 0 ];then
-        env=" "
-    else
-        env="export CUDA_VISIBLE_DEVICES=${GPUID}"
-    fi
-    # set CUDA_VISIBLE_DEVICES
-    eval $env
-    export Count=0
-    IFS="|"
-    #run serving
-    func_serving "${web_service_cmd}"
-
-
-
-else
-    IFS="|"
-    export Count=0
-    USE_GPU_KEY=(${train_use_gpu_value})
-    for gpu in ${gpu_list[*]}; do
-        use_gpu=${USE_GPU_KEY[Count]}
-        Count=$(($Count + 1))
-        if [ ${gpu} = "-1" ];then
-            env=""
-        elif [ ${#gpu} -le 1 ];then
-            env="export CUDA_VISIBLE_DEVICES=${gpu}"
-            eval ${env}
-        elif [ ${#gpu} -le 15 ];then
-            IFS=","
-            array=(${gpu})
-            env="export CUDA_VISIBLE_DEVICES=${array[0]}"
-            IFS="|"
-        else
-            IFS=";"
-            array=(${gpu})
-            ips=${array[0]}
-            gpu=${array[1]}
-            IFS="|"
-            env=" "
-        fi
-        for autocast in ${autocast_list[*]}; do 
-            for trainer in ${trainer_list[*]}; do 
-                flag_quant=False
-                if [ ${trainer} = ${pact_key} ]; then
-                    run_train=${pact_trainer}
-                    run_export=${pact_export}
-                    flag_quant=True
-                elif [ ${trainer} = "${fpgm_key}" ]; then
-                    run_train=${fpgm_trainer}
-                    run_export=${fpgm_export}
-                elif [ ${trainer} = "${distill_key}" ]; then
-                    run_train=${distill_trainer}
-                    run_export=${distill_export}
-                elif [ ${trainer} = ${trainer_key1} ]; then
-                    run_train=${trainer_value1}
-                    run_export=${export_value1}
-                elif [[ ${trainer} = ${trainer_key2} ]]; then
-                    run_train=${trainer_value2}
-                    run_export=${export_value2}
-                else
-                    run_train=${norm_trainer}
-                    run_export=${norm_export}
-                fi
-
-                if [ ${run_train} = "null" ]; then
-                    continue
-                fi
-                
-                set_autocast=$(func_set_params "${autocast_key}" "${autocast}")
-                set_epoch=$(func_set_params "${epoch_key}" "${epoch_num}")
-                set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}")
-                set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}")
-                set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}")
-                set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${use_gpu}")
-                save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
-                
-                # load pretrain from norm training if current trainer is pact or fpgm trainer
-                if [ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]; then
-                    set_pretrain="${load_norm_train_model}"
-                fi
-
-                set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
-                if [ ${#gpu} -le 2 ];then  # train with cpu or single gpu
-                    cmd="${python} ${run_train} ${set_use_gpu}  ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} "
-                elif [ ${#gpu} -le 15 ];then  # train with multi-gpu
-                    cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1}"
-                else     # train with multi-machine
-                    cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1}"
-                fi
-                # run train
-                eval "unset CUDA_VISIBLE_DEVICES"
-                eval $cmd
-                status_check $? "${cmd}" "${status_log}"
-
-                set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}")
-                # save norm trained models to set pretrain for pact training and fpgm training 
-                if [ ${trainer} = ${trainer_norm} ]; then
-                    load_norm_train_model=${set_eval_pretrain}
-                fi
-                # run eval 
-                if [ ${eval_py} != "null" ]; then
-                    set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}")
-                    eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu} ${set_eval_params1}" 
-                    eval $eval_cmd
-                    status_check $? "${eval_cmd}" "${status_log}"
-                fi
-                # run export model
-                if [ ${run_export} != "null" ]; then 
-                    # run export model
-                    save_infer_path="${save_log}"
-                    set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${train_model_name}")
-                    set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_path}")
-                    export_cmd="${python} ${run_export} ${set_export_weight} ${set_save_infer_key}"
-                    eval $export_cmd
-                    status_check $? "${export_cmd}" "${status_log}"
-
-                    #run inference
-                    eval $env
-                    save_infer_path="${save_log}"
-                    func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}"
-                    eval "unset CUDA_VISIBLE_DEVICES"
-                fi
-            done  # done with:    for trainer in ${trainer_list[*]}; do 
-        done      # done with:    for autocast in ${autocast_list[*]}; do 
-    done          # done with:    for gpu in ${gpu_list[*]}; do
-fi  # end if [ ${MODE} = "infer" ]; then

tests/test_python.sh

@@ -2,7 +2,14 @@
 source tests/common_func.sh
 
 FILENAME=$1
-dataline=$(awk 'NR==1, NR==51{print}'  $FILENAME)
+# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer', 'klquant_infer']
+MODE=$2
+
+if [ ${MODE} = "klquant_infer" ]; then
+    dataline=$(awk 'NR==82, NR==98{print}'  $FILENAME)
+else
+    dataline=$(awk 'NR==1, NR==51{print}'  $FILENAME)
+fi
 
 # parser params
 IFS=$'\n'
@@ -84,6 +91,35 @@ benchmark_value=$(func_parser_value "${lines[49]}")
 infer_key1=$(func_parser_key "${lines[50]}")
 infer_value1=$(func_parser_value "${lines[50]}")
 
+# parser klquant_infer
+if [ ${MODE} = "klquant_infer" ]; then
+    # parser inference model 
+    infer_model_dir_list=$(func_parser_value "${lines[1]}")
+    infer_export_list=$(func_parser_value "${lines[2]}")
+    infer_is_quant=$(func_parser_value "${lines[3]}")
+    # parser inference 
+    inference_py=$(func_parser_value "${lines[4]}")
+    use_gpu_key=$(func_parser_key "${lines[5]}")
+    use_gpu_list=$(func_parser_value "${lines[5]}")
+    use_mkldnn_key=$(func_parser_key "${lines[6]}")
+    use_mkldnn_list=$(func_parser_value "${lines[6]}")
+    cpu_threads_key=$(func_parser_key "${lines[7]}")
+    cpu_threads_list=$(func_parser_value "${lines[7]}")
+    batch_size_key=$(func_parser_key "${lines[8]}")
+    batch_size_list=$(func_parser_value "${lines[8]}")
+    use_trt_key=$(func_parser_key "${lines[9]}")
+    use_trt_list=$(func_parser_value "${lines[9]}")
+    precision_key=$(func_parser_key "${lines[10]}")
+    precision_list=$(func_parser_value "${lines[10]}")
+    infer_model_key=$(func_parser_key "${lines[11]}")
+    image_dir_key=$(func_parser_key "${lines[12]}")
+    infer_img_dir=$(func_parser_value "${lines[12]}")
+    save_log_key=$(func_parser_key "${lines[13]}")
+    benchmark_key=$(func_parser_key "${lines[14]}")
+    benchmark_value=$(func_parser_value "${lines[14]}")
+    infer_key1=$(func_parser_key "${lines[15]}")
+    infer_value1=$(func_parser_value "${lines[15]}")
+fi
 
 LOG_PATH="./tests/output"
 mkdir -p ${LOG_PATH}
@@ -158,16 +194,148 @@ function func_inference(){
     done
 }
 
-
-# set cuda device
-GPUID=$2
-if [ ${#GPUID} -le 0 ];then
+if [ ${MODE} = "infer" ] || [ ${MODE} = "klquant_infer" ]; then
+    GPUID=$3
+    if [ ${#GPUID} -le 0 ];then
         env=" "
-else
+    else
         env="export CUDA_VISIBLE_DEVICES=${GPUID}"
-fi
-set CUDA_VISIBLE_DEVICES
-eval $env
+    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=${infer_quant_flag[Count]}
+        func_inference "${python}" "${inference_py}" "${save_infer_dir}" "${LOG_PATH}" "${infer_img_dir}" ${is_quant}
+        Count=$(($Count + 1))
+    done
+else
+    IFS="|"
+    export Count=0
+    USE_GPU_KEY=(${train_use_gpu_value})
+    for gpu in ${gpu_list[*]}; do
+        use_gpu=${USE_GPU_KEY[Count]}
+        Count=$(($Count + 1))
+        if [ ${gpu} = "-1" ];then
+            env=""
+        elif [ ${#gpu} -le 1 ];then
+            env="export CUDA_VISIBLE_DEVICES=${gpu}"
+            eval ${env}
+        elif [ ${#gpu} -le 15 ];then
+            IFS=","
+            array=(${gpu})
+            env="export CUDA_VISIBLE_DEVICES=${array[0]}"
+            IFS="|"
+        else
+            IFS=";"
+            array=(${gpu})
+            ips=${array[0]}
+            gpu=${array[1]}
+            IFS="|"
+            env=" "
+        fi
+        for autocast in ${autocast_list[*]}; do 
+            for trainer in ${trainer_list[*]}; do 
+                flag_quant=False
+                if [ ${trainer} = ${pact_key} ]; then
+                    run_train=${pact_trainer}
+                    run_export=${pact_export}
+                    flag_quant=True
+                elif [ ${trainer} = "${fpgm_key}" ]; then
+                    run_train=${fpgm_trainer}
+                    run_export=${fpgm_export}
+                elif [ ${trainer} = "${distill_key}" ]; then
+                    run_train=${distill_trainer}
+                    run_export=${distill_export}
+                elif [ ${trainer} = ${trainer_key1} ]; then
+                    run_train=${trainer_value1}
+                    run_export=${export_value1}
+                elif [[ ${trainer} = ${trainer_key2} ]]; then
+                    run_train=${trainer_value2}
+                    run_export=${export_value2}
+                else
+                    run_train=${norm_trainer}
+                    run_export=${norm_export}
+                fi
+
+                if [ ${run_train} = "null" ]; then
+                    continue
+                fi
                 
+                set_autocast=$(func_set_params "${autocast_key}" "${autocast}")
+                set_epoch=$(func_set_params "${epoch_key}" "${epoch_num}")
+                set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}")
+                set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}")
+                set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}")
+                set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${use_gpu}")
+                save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
+                
+                # load pretrain from norm training if current trainer is pact or fpgm trainer
+                if [ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]; then
+                    set_pretrain="${load_norm_train_model}"
+                fi
+
+                set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
+                if [ ${#gpu} -le 2 ];then  # train with cpu or single gpu
+                    cmd="${python} ${run_train} ${set_use_gpu}  ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} "
+                elif [ ${#gpu} -le 15 ];then  # train with multi-gpu
+                    cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1}"
+                else     # train with multi-machine
+                    cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1}"
+                fi
+                # run train
+                eval "unset CUDA_VISIBLE_DEVICES"
+                eval $cmd
+                status_check $? "${cmd}" "${status_log}"
+
+                set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}")
+                # save norm trained models to set pretrain for pact training and fpgm training 
+                if [ ${trainer} = ${trainer_norm} ]; then
+                    load_norm_train_model=${set_eval_pretrain}
+                fi
+                # run eval 
+                if [ ${eval_py} != "null" ]; then
+                    set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}")
+                    eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu} ${set_eval_params1}" 
+                    eval $eval_cmd
+                    status_check $? "${eval_cmd}" "${status_log}"
+                fi
+                # run export model
+                if [ ${run_export} != "null" ]; then 
+                    # run export model
+                    save_infer_path="${save_log}"
+                    set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${train_model_name}")
+                    set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_path}")
+                    export_cmd="${python} ${run_export} ${set_export_weight} ${set_save_infer_key}"
+                    eval $export_cmd
+                    status_check $? "${export_cmd}" "${status_log}"
+
+                    #run inference
+                    eval $env
+                    save_infer_path="${save_log}"
+                    func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}"
+                    eval "unset CUDA_VISIBLE_DEVICES"
+                fi
+            done  # done with:    for trainer in ${trainer_list[*]}; do 
+        done      # done with:    for autocast in ${autocast_list[*]}; do 
+    done          # done with:    for gpu in ${gpu_list[*]}; do
+fi  # end if [ ${MODE} = "infer" ]; then
 
-echo "################### run test ###################"