#!/bin/bash source test_tipc/common_func.sh FILENAME=$1 # MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer', 'whole_infer'] MODE=$2 dataline=$(awk 'NR==1, NR==51{print}' $FILENAME) # parser params IFS=$'\n' lines=(${dataline}) # The training params model_name=$(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 "${lines[6]}" "${MODE}") save_model_key=$(func_parser_key "${lines[7]}") train_batch_key=$(func_parser_key "${lines[8]}") train_batch_value=$(func_parser_params "${lines[8]}" "${MODE}") pretrain_model_key=$(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 "${lines[34]}") inference_dir=$(func_parser_value "${lines[35]}") # parser inference model infer_model_dir_list=$(func_parser_value "${lines[36]}") infer_export_list=$(func_parser_value "${lines[37]}") infer_is_quant=$(func_parser_value "${lines[38]}") # parser inference inference_py=$(func_parser_value "${lines[39]}") use_gpu_key=$(func_parser_key "${lines[40]}") use_gpu_list=$(func_parser_value "${lines[40]}") use_mkldnn_key=$(func_parser_key "${lines[41]}") use_mkldnn_list=$(func_parser_value "${lines[41]}") cpu_threads_key=$(func_parser_key "${lines[42]}") cpu_threads_list=$(func_parser_value "${lines[42]}") batch_size_key=$(func_parser_key "${lines[43]}") batch_size_list=$(func_parser_value "${lines[43]}") use_trt_key=$(func_parser_key "${lines[44]}") use_trt_list=$(func_parser_value "${lines[44]}") precision_key=$(func_parser_key "${lines[45]}") precision_list=$(func_parser_value "${lines[45]}") infer_model_key=$(func_parser_key "${lines[46]}") image_dir_key=$(func_parser_key "${lines[47]}") infer_img_dir=$(func_parser_value "${lines[47]}") save_log_key=$(func_parser_key "${lines[48]}") benchmark_key=$(func_parser_key "${lines[49]}") benchmark_value=$(func_parser_value "${lines[49]}") infer_key1=$(func_parser_key "${lines[50]}") infer_value1=$(func_parser_value "${lines[50]}") LOG_PATH="./test_tipc/output/${model_name}/${MODE}" mkdir -p ${LOG_PATH} status_log="${LOG_PATH}/results_python.log" function func_inference(){ IFS='|' _python=$1 _script=$2 _model_dir=$3 _log_path=$4 _img_dir=$5 _flag_quant=$6 _gpu=$7 # 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 for precision in ${precision_list[*]}; do if [ ${use_mkldnn} = "False" ] && [ ${precision} = "fp16" ]; then continue fi # skip when enable fp16 but disable mkldnn if [ ${_flag_quant} = "True" ] && [ ${precision} != "int8" ]; then continue fi # skip when quant model inference but precision is not int8 set_precision=$(func_set_params "${precision_key}" "${precision}") _save_log_path="${_log_path}/python_infer_cpu_gpus_${_gpu}_usemkldnn_${use_mkldnn}_threads_${threads}_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}" "${batch_size}") set_mkldnn=$(func_set_params "${use_mkldnn_key}" "${use_mkldnn}") set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}") set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}") set_infer_params0=$(func_set_params "${save_log_key}" "${save_log_value}") set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}") command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${set_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_params0} ${set_infer_data} ${set_benchmark} ${set_precision} ${set_infer_params1} > ${_save_log_path} 2>&1 " eval $command last_status=${PIPESTATUS[0]} eval "cat ${_save_log_path}" status_check $last_status "${command}" "${status_log}" "${model_name}" "${_save_log_path}" done 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}/python_infer_gpu_gpus_${_gpu}_usetrt_${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_params0=$(func_set_params "${save_log_key}" "${save_log_value}") set_infer_params1=$(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} ${set_infer_params0} > ${_save_log_path} 2>&1 " eval $command last_status=${PIPESTATUS[0]} eval "cat ${_save_log_path}" status_check $last_status "${command}" "${status_log}" "${model_name}" "${_save_log_path}" done done done else echo "Does not support hardware other than CPU and GPU Currently!" fi done } 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 $env export Count=0 gpu=0 IFS="|" infer_run_exports=(${infer_export_list}) infer_quant_flag=(${infer_is_quant}) for infer_model in ${infer_model_dir_list[*]}; do # run export if [ ${infer_run_exports[Count]} != "null" ];then save_infer_dir="${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_log_path="${LOG_PATH}_export_${Count}.log" export_cmd="${python} ${infer_run_exports[Count]} ${set_export_weight} ${set_save_infer_key} > ${export_log_path} 2>&1 " echo ${infer_run_exports[Count]} echo $export_cmd eval $export_cmd status_export=$? status_check $status_export "${export_cmd}" "${status_log}" "${model_name}" "${export_log_path}" else save_infer_dir=${infer_model} fi #run inference is_quant=${infer_quant_flag[Count]} func_inference "${python}" "${inference_py}" "${save_infer_dir}" "${LOG_PATH}" "${infer_img_dir}" ${is_quant} "${gpu}" Count=$(($Count + 1)) done else IFS="|" export Count=0 USE_GPU_KEY=(${train_use_gpu_value}) for gpu in ${gpu_list[*]}; do train_use_gpu=${USE_GPU_KEY[Count]} Count=$(($Count + 1)) ips="" if [ ${gpu} = "-1" ];then env="" elif [ ${#gpu} -le 1 ];then env="export CUDA_VISIBLE_DEVICES=${gpu}" elif [ ${#gpu} -le 15 ];then IFS="," array=(${gpu}) env="export CUDA_VISIBLE_DEVICES=${array[0]}" IFS="|" else IFS=";" array=(${gpu}) ips=${array[0]} gpu=${array[1]} IFS="|" env=" " fi for autocast in ${autocast_list[*]}; do if [ ${autocast} = "amp" ]; then set_amp_config="Global.use_amp=True Global.scale_loss=1024.0 Global.use_dynamic_loss_scaling=True" else set_amp_config=" " fi 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 [ ${run_train} = "null" ]; then continue fi set_epoch=$(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 "${train_use_gpu_key}" "${train_use_gpu}") # if length of ips >= 15, then it is seen as multi-machine # 15 is the min length of ips info for multi-machine: 0.0.0.0,0.0.0.0 if [ ${#ips} -le 15 ];then save_log="${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 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_batchsize} ${set_train_params1} ${set_amp_config} " elif [ ${#ips} -le 15 ];then # train with multi-gpu cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_batchsize} ${set_train_params1} ${set_amp_config}" else # train with multi-machine cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_batchsize} ${set_train_params1} ${set_amp_config}" fi # run train eval $cmd eval "cat ${save_log}/train.log >> ${save_log}.log" status_check $? "${cmd}" "${status_log}" "${model_name}" "${save_log}.log" set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}") # run eval if [ ${eval_py} != "null" ]; then eval ${env} set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}") eval_log_path="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}_eval.log" eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu} ${set_eval_params1} > ${eval_log_path} 2>&1 " eval $eval_cmd status_check $? "${eval_cmd}" "${status_log}" "${model_name}" "${eval_log_path}" fi # run export model if [ ${run_export} != "null" ]; then # run export model save_infer_path="${save_log}" export_log_path="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}_export.log" set_export_weight=$(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} > ${export_log_path} 2>&1 " eval $export_cmd status_check $? "${export_cmd}" "${status_log}" "${model_name}" "${export_log_path}" #run inference eval $env save_infer_path="${save_log}" 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}" "${gpu}" 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