366 lines
16 KiB
Bash
366 lines
16 KiB
Bash
#!/bin/bash
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FILENAME=$1
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# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer']
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MODE=$2
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dataline=$(cat ${FILENAME})
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# parser params
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IFS=$'\n'
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lines=(${dataline})
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function func_parser_key(){
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strs=$1
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IFS=":"
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array=(${strs})
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tmp=${array[0]}
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echo ${tmp}
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}
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function func_parser_value(){
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strs=$1
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IFS=":"
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array=(${strs})
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tmp=${array[1]}
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echo ${tmp}
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}
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function func_set_params(){
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key=$1
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value=$2
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if [ ${key} = "null" ];then
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echo " "
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elif [[ ${value} = "null" ]] || [[ ${value} = " " ]] || [ ${#value} -le 0 ];then
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echo " "
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else
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echo "${key}=${value}"
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fi
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}
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function func_parser_params(){
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strs=$1
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IFS=":"
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array=(${strs})
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key=${array[0]}
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tmp=${array[1]}
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IFS="|"
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res=""
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for _params in ${tmp[*]}; do
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IFS="="
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array=(${_params})
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mode=${array[0]}
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value=${array[1]}
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if [[ ${mode} = ${MODE} ]]; then
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IFS="|"
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#echo $(func_set_params "${mode}" "${value}")
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echo $value
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break
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fi
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IFS="|"
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done
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echo ${res}
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}
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function status_check(){
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last_status=$1 # the exit code
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run_command=$2
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run_log=$3
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if [ $last_status -eq 0 ]; then
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echo -e "\033[33m Run successfully with command - ${run_command}! \033[0m" | tee -a ${run_log}
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else
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echo -e "\033[33m Run failed with command - ${run_command}! \033[0m" | tee -a ${run_log}
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fi
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}
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IFS=$'\n'
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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.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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if [ ${MODE} = "infer" ]; then
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GPUID=$3
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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 $env
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export Count=0
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IFS="|"
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infer_run_exports=(${infer_export_list})
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infer_quant_flag=(${infer_is_quant})
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for infer_model in ${infer_model_dir_list[*]}; do
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# run export
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if [ ${infer_run_exports[Count]} != "null" ];then
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save_infer_dir=$(dirname $infer_model)
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set_export_weight=$(func_set_params "${export_weight}" "${infer_model}")
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set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_dir}")
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export_cmd="${python} ${norm_export} ${set_export_weight} ${set_save_infer_key}"
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eval $export_cmd
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status_export=$?
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if [ ${status_export} = 0 ];then
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status_check $status_export "${export_cmd}" "${status_log}"
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fi
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else
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save_infer_dir=${infer_model}
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fi
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#run inference
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is_quant=${infer_quant_flag[Count]}
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func_inference "${python}" "${inference_py}" "${save_infer_dir}" "${LOG_PATH}" "${infer_img_dir}" ${is_quant}
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Count=$(($Count + 1))
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done
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else
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IFS="|"
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export Count=0
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USE_GPU_KEY=(${train_use_gpu_value})
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for gpu in ${gpu_list[*]}; do
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use_gpu=${USE_GPU_KEY[Count]}
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Count=$(($Count + 1))
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if [ ${gpu} = "-1" ];then
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env=""
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elif [ ${#gpu} -le 1 ];then
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env="export CUDA_VISIBLE_DEVICES=${gpu}"
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eval ${env}
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elif [ ${#gpu} -le 15 ];then
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IFS=","
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array=(${gpu})
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env="export CUDA_VISIBLE_DEVICES=${array[0]}"
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IFS="|"
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else
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IFS=";"
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array=(${gpu})
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ips=${array[0]}
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gpu=${array[1]}
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IFS="|"
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env=" "
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fi
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for autocast in ${autocast_list[*]}; do
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for trainer in ${trainer_list[*]}; do
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flag_quant=False
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if [ ${trainer} = ${pact_key} ]; then
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run_train=${pact_trainer}
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run_export=${pact_export}
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flag_quant=True
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elif [ ${trainer} = "${fpgm_key}" ]; then
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run_train=${fpgm_trainer}
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run_export=${fpgm_export}
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elif [ ${trainer} = "${distill_key}" ]; then
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run_train=${distill_trainer}
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run_export=${distill_export}
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elif [ ${trainer} = ${trainer_key1} ]; then
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run_train=${trainer_value1}
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run_export=${export_value1}
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elif [[ ${trainer} = ${trainer_key2} ]]; then
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run_train=${trainer_value2}
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run_export=${export_value2}
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else
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run_train=${norm_trainer}
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run_export=${norm_export}
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fi
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if [ ${run_train} = "null" ]; then
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continue
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fi
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set_autocast=$(func_set_params "${autocast_key}" "${autocast}")
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set_epoch=$(func_set_params "${epoch_key}" "${epoch_num}")
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set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}")
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set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}")
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set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}")
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set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${use_gpu}")
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save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
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# load pretrain from norm training if current trainer is pact or fpgm trainer
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if [ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]; then
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set_pretrain="${load_norm_train_model}"
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fi
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set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
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if [ ${#gpu} -le 2 ];then # train with cpu or single gpu
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cmd="${python} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} "
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elif [ ${#gpu} -le 15 ];then # train with multi-gpu
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cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1}"
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else # train with multi-machine
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cmd="${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}"
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fi
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# run train
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eval "unset CUDA_VISIBLE_DEVICES"
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eval $cmd
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status_check $? "${cmd}" "${status_log}"
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set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}")
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# save norm trained models to set pretrain for pact training and fpgm training
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if [ ${trainer} = ${trainer_norm} ]; then
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load_norm_train_model=${set_eval_pretrain}
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fi
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# run eval
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if [ ${eval_py} != "null" ]; then
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set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}")
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eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu} ${set_eval_params1}"
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eval $eval_cmd
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status_check $? "${eval_cmd}" "${status_log}"
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fi
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# run export model
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if [ ${run_export} != "null" ]; then
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# run export model
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save_infer_path="${save_log}"
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set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${train_model_name}")
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set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_path}")
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export_cmd="${python} ${run_export} ${set_export_weight} ${set_save_infer_key}"
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eval $export_cmd
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status_check $? "${export_cmd}" "${status_log}"
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#run inference
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eval $env
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save_infer_path="${save_log}"
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func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}"
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eval "unset CUDA_VISIBLE_DEVICES"
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fi
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done # done with: for trainer in ${trainer_list[*]}; do
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done # done with: for autocast in ${autocast_list[*]}; do
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done # done with: for gpu in ${gpu_list[*]}; do
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fi # end if [ ${MODE} = "infer" ]; then
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