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add benchmark_trainv2.sh
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LDOUBLEV
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test_tipc/benchmark_trainv2.sh
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224
test_tipc/benchmark_trainv2.sh
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#!/bin/bash
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source test_tipc/common_func.sh
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# run benchmark sh
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# Usage:
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# bash run_benchmark_train.sh config.txt params
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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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tmp=${array[1]}
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echo ${tmp}
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}
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function func_sed_params(){
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filename=$1
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line=$2
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param_value=$3
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params=`sed -n "${line}p" $filename`
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IFS=":"
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array=(${params})
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key=${array[0]}
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value=${array[1]}
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if [[ $value =~ 'benchmark_train' ]];then
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IFS='='
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_val=(${value})
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param_value="${_val[0]}=${param_value}"
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fi
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new_params="${key}:${param_value}"
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IFS=";"
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cmd="sed -i '${line}s/.*/${new_params}/' '${filename}'"
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eval $cmd
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}
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function set_gpu_id(){
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string=$1
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_str=${string:1:6}
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IFS="C"
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arr=(${_str})
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M=${arr[0]}
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P=${arr[1]}
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gn=`expr $P - 1`
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gpu_num=`expr $gn / $M`
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seq=`seq -s "," 0 $gpu_num`
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echo $seq
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}
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function get_repo_name(){
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IFS=";"
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cur_dir=$(pwd)
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IFS="/"
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arr=(${cur_dir})
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echo ${arr[-1]}
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}
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FILENAME=$1
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# MODE be one of ['benchmark_train']
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MODE=$2
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params=$3
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# bash test_tipc/benchmark_train.sh test_tipc/configs/det_mv3_db_v2.0/train_benchmark.txt benchmark_train dynamic_bs8_null_SingleP_DP_N1C1
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IFS="\n"
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# parser params from input: modeltype_bs${bs_item}_${fp_item}_${run_process_type}_${run_mode}_${device_num}
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IFS="_"
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params_list=(${params})
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model_type=${params_list[0]}
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batch_size=${params_list[1]}
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batch_size=`echo ${batch_size} | tr -cd "[0-9]" `
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precision=${params_list[2]}
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run_process_type=${params_list[3]}
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run_mode=${params_list[4]}
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device_num=${params_list[5]}
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device_num_copy=$device_num
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IFS=";"
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# sed batchsize and precision
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func_sed_params "$FILENAME" "6" "$precision"
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func_sed_params "$FILENAME" "9" "$batch_size"
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# parser params from train_benchmark.txt
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dataline=`cat $FILENAME`
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# parser params
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IFS=$'\n'
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lines=(${dataline})
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model_name=$(func_parser_value "${lines[1]}")
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# 获取benchmark_params所在的行数
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line_num=`grep -n "benchmark_params" $FILENAME | cut -d ":" -f 1`
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# for train log parser
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line_num=`expr $line_num + 1`
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profile_option_key=$(func_parser_key "${lines[line_num]}")
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profile_option_params=$(func_parser_value "${lines[line_num]}")
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profile_option="${profile_option_key}:${profile_option_params}"
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line_num=`expr $line_num + 1`
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flags_value=$(func_parser_value "${lines[line_num]}")
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gpu_id=$(set_gpu_id $device_num)
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repo_name=$(get_repo_name )
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SAVE_LOG=${BENCHMARK_LOG_DIR:-$(pwd)} # */benchmark_log
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status_log="${SAVE_LOG}/benchmark_log/results.log"
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# set export
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IFS=";"
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flags_list=(${flags_value})
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for _flag in ${flags_list[*]}; do
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cmd="export ${_flag}"
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eval $cmd
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done
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if [ ${precision} = "null" ];then
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precision="fp32"
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fi
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# set env
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python=python3.7
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export model_branch=`git symbolic-ref HEAD 2>/dev/null | cut -d"/" -f 3`
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export model_commit=$(git log|head -n1|awk '{print $2}')
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export str_tmp=$(echo `pip list|grep paddlepaddle-gpu|awk -F ' ' '{print $2}'`)
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export frame_version=${str_tmp%%.post*}
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export frame_commit=$(echo `${python} -c "import paddle;print(paddle.version.commit)"`)
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# set eval and export as null
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# line eval_py: 24
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# line export_py: 30
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func_sed_params "$FILENAME" "24" "null"
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func_sed_params "$FILENAME" "30" "null"
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if [ ${#gpu_id} -le 1 ];then
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log_path="$SAVE_LOG/profiling_log"
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mkdir -p $log_path
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log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_profiling"
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func_sed_params "$FILENAME" "4" "0" # sed used gpu_id
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# set profile_option params
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IFS=";"
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cmd="sed -i '13s/.*/${profile_option}/' '${FILENAME}'"
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eval $cmd
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# run test_train_inference_python.sh
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cmd="bash test_tipc/test_train_inference_python.sh ${FILENAME} benchmark_train > ${log_path}/${log_name} 2>&1 "
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echo $cmd
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eval $cmd
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eval "cat ${log_path}/${log_name}"
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# without profile
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log_path="$SAVE_LOG/train_log"
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speed_log_path="$SAVE_LOG/index"
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mkdir -p $log_path
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mkdir -p $speed_log_path
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log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_log"
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speed_log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_speed"
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func_sed_params "$FILENAME" "13" "null" # sed profile_id as null
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cmd="bash test_tipc/test_train_inference_python.sh ${FILENAME} benchmark_train > ${log_path}/${log_name} 2>&1 "
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echo $cmd
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job_bt=`date '+%Y%m%d%H%M%S'`
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eval $cmd
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job_et=`date '+%Y%m%d%H%M%S'`
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export model_run_time=$((${job_et}-${job_bt}))
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eval "cat ${log_path}/${log_name}"
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# parser log
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_model_name="${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}"
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# cmd="${python} ${BENCHMARK_ROOT}/scripts/analysis.py --filename ${log_path}/${log_name} \
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cmd="${python} analysis.py --filename ${log_path}/${log_name} \
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--speed_log_file '${speed_log_path}/${speed_log_name}' \
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--model_name ${_model_name} \
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--base_batch_size ${batch_size} \
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--run_mode ${run_mode} \
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--run_process_type ${run_process_type} \
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--fp_item ${precision} \
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--keyword ips: \
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--skip_steps 2 \
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--device_num ${device_num} \
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--speed_unit images/s \
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--convergence_key loss: "
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echo $cmd
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eval $cmd
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last_status=${PIPESTATUS[0]}
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status_check $last_status "${cmd}" "${status_log}"
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else
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unset_env=`unset CUDA_VISIBLE_DEVICES`
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log_path="$SAVE_LOG/train_log"
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speed_log_path="$SAVE_LOG/index"
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mkdir -p $log_path
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mkdir -p $speed_log_path
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log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_log"
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speed_log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_speed"
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func_sed_params "$FILENAME" "4" "$gpu_id" # sed used gpu_id
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func_sed_params "$FILENAME" "13" "null" # sed --profile_option as null
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cmd="bash test_tipc/test_train_inference_python.sh ${FILENAME} benchmark_train > ${log_path}/${log_name} 2>&1 "
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echo $cmd
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job_bt=`date '+%Y%m%d%H%M%S'`
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eval $cmd
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job_et=`date '+%Y%m%d%H%M%S'`
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export model_run_time=$((${job_et}-${job_bt}))
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eval "cat ${log_path}/${log_name}"
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# parser log
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_model_name="${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}"
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# cmd="python3.7 ${BENCHMARK_ROOT}/scripts/analysis.py --filename ${log_path}/${log_name} \
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cmd="${python} analysis.py --filename ${log_path}/${log_name} \
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--speed_log_file '${speed_log_path}/${speed_log_name}' \
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--model_name ${_model_name} \
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--base_batch_size ${batch_size} \
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--run_mode ${run_mode} \
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--run_process_type ${run_process_type} \
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--fp_item ${precision} \
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--keyword ips: \
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--skip_steps 2 \
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--device_num ${device_num} \
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--speed_unit images/s \
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--convergence_key loss: "
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echo $cmd
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eval $cmd
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last_status=${PIPESTATUS[0]}
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status_check $last_status "${cmd}" "${status_log}"
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fi
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@ -4,9 +4,9 @@ python:python3.7
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gpu_list:0|0,1
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gpu_list:0|0,1
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Global.use_gpu:True|True
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Global.use_gpu:True|True
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Global.auto_cast:null
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Global.auto_cast:null
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Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
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Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300|benchmark_train=2
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Global.save_model_dir:./output/
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Global.save_model_dir:./output/
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Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
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Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4|benchmark_train=16
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Global.pretrained_model:null
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Global.pretrained_model:null
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train_model_name:latest
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train_model_name:latest
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train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
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train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
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@ -49,3 +49,6 @@ inference:tools/infer/predict_det.py
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null:null
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null:null
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--benchmark:True
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--benchmark:True
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null:null
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null:null
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===========================benchmark_params==========================
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--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
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flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
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