fix comments
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LDOUBLEV
parent
09e7211082
commit
b4d7be33f0
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@ -14,7 +14,7 @@ null:null
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##
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trainer:norm_train|pact_train
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norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
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pact_train:deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy
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pact_train:deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o
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fpgm_train:null
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distill_train:null
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null:null
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@ -25,34 +25,27 @@ function func_parser_value(){
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IFS=$'\n'
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# The training params
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model_name=$(func_parser_value "${lines[1]}")
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train_model_list=$(func_parser_value "${lines[1]}")
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trainer_list=$(func_parser_value "${lines[14]}")
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# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer']
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MODE=$2
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# prepare pretrained weights and dataset
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wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
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wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar
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cd pretrain_models && tar xf det_mv3_db_v2.0_train.tar && cd ../
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if [ ${MODE} = "lite_train_infer" ];then
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# pretrain lite train data
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wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
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rm -rf ./train_data/icdar2015
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wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
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cd ./train_data/ && tar xf icdar2015_lite.tar
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ln -s ./icdar2015_lite ./icdar2015
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cd ../
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epoch=10
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eval_batch_step=10
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elif [ ${MODE} = "whole_train_infer" ];then
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wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
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rm -rf ./train_data/icdar2015
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wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar
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cd ./train_data/ && tar xf icdar2015.tar && cd ../
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epoch=500
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eval_batch_step=200
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elif [ ${MODE} = "whole_infer" ];then
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wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
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rm -rf ./train_data/icdar2015
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wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_infer.tar
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cd ./train_data/ && tar xf icdar2015_infer.tar
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219
tests/test.sh
219
tests/test.sh
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@ -182,10 +182,7 @@ function func_inference(){
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if [ ${use_trt} = "False" ] && [ ${precision} != "fp32" ]; then
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continue
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fi
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if [ ${use_trt} = "False" ] && [ ${_flag_quant} = "True" ]; then
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continue
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fi
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if [ ${precision} != "int8" ] && [ ${_flag_quant} = "True" ]; then
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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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@ -208,108 +205,7 @@ function func_inference(){
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done
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}
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if [ ${MODE} != "infer" ]; then
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IFS="|"
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export Count=0
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USE_GPU_KEY=(${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_${autocast}"
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set_save_model=$(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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# 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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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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export_cmd="${python} ${run_export} ${export_weight}=${save_log}/${train_model_name} ${save_infer_key}=${save_infer_path}"
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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
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done
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done
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else
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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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@ -319,5 +215,114 @@ else
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echo $env
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#run inference
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func_inference "${python}" "${inference_py}" "${infer_model}" "${LOG_PATH}" "${infer_img_dir}" "False"
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fi
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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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export_cmd="${python} ${run_export} ${export_weight}=${save_log}/${train_model_name} ${save_infer_key}=${save_infer_path}"
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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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