Merge pull request #157 from iclementine/cice

add ci/ce scripts

examples/parallelwave_gan/baker/config.py

@@ -13,9 +13,12 @@
 # limitations under the License.
 
 import yaml
+from pathlib import Path
 from yacs.config import CfgNode as Configuration
 
-with open("conf/default.yaml", 'rt') as f:
+config_path = (Path(__file__).parent / "conf" / "default.yaml").resolve()
+
+with open(config_path, 'rt') as f:
     _C = yaml.safe_load(f)
     _C = Configuration(_C)
 

examples/speedyspeech/baker/config.py

@@ -18,7 +18,7 @@ from pathlib import Path
 
 config_path = (Path(__file__).parent / "conf" / "default.yaml").resolve()
 
-with open("conf/default.yaml", 'rt') as f:
+with open(config_path, 'rt') as f:
     _C = yaml.safe_load(f)
     _C = Configuration(_C)
 

examples/speedyspeech/baker/inference.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
 import argparse
 from pathlib import Path
 
@@ -31,6 +32,8 @@ def main():
         type=str,
         help="text to synthesize, a 'utt_id sentence' pair per line")
     parser.add_argument("--output-dir", type=str, help="output dir")
+    parser.add_argument(
+        "--enable-auto-log", action="store_true", help="use auto log")
 
     args, _ = parser.parse_known_args()
 
@@ -48,6 +51,23 @@ def main():
     pwg_config.enable_memory_optim()
     pwg_predictor = inference.create_predictor(pwg_config)
 
+    if args.enable_auto_log:
+        import auto_log
+        os.makedirs("output", exist_ok=True)
+        pid = os.getpid()
+        logger = auto_log.AutoLogger(
+            model_name="speedyspeech",
+            model_precision='float32',
+            batch_size=1,
+            data_shape="dynamic",
+            save_path="./output/auto_log.log",
+            inference_config=speedyspeech_config,
+            pids=pid,
+            process_name=None,
+            gpu_ids=0,
+            time_keys=['preprocess_time', 'inference_time', 'postprocess_time'],
+            warmup=0)
+
     output_dir = Path(args.output_dir)
     output_dir.mkdir(parents=True, exist_ok=True)
     sentences = []
@@ -57,10 +77,16 @@ def main():
             sentences.append((utt_id, sentence))
 
     for utt_id, sentence in sentences:
+        if args.enable_auto_log:
+            logger.times.start()
+
         phones, tones = text_analysis(sentence)
         phones = phones.numpy()
         tones = tones.numpy()
 
+        if args.enable_auto_log:
+            logger.times.stamp()
+
         input_names = speedyspeech_predictor.get_input_names()
         phones_handle = speedyspeech_predictor.get_input_handle(input_names[0])
         tones_handle = speedyspeech_predictor.get_input_handle(input_names[1])
@@ -86,9 +112,18 @@ def main():
         output_handle = pwg_predictor.get_output_handle(output_names[0])
         wav = output_data = output_handle.copy_to_cpu()
 
+        if args.enable_auto_log:
+            logger.times.stamp()
+
         sf.write(output_dir / (utt_id + ".wav"), wav, samplerate=24000)
+
+        if args.enable_auto_log:
+            logger.times.end(stamp=True)
         print(f"{utt_id} done!")
 
+    if args.enable_auto_log:
+        logger.report()
+
 
 if __name__ == "__main__":
     main()

tests/prepare.sh

@@ -0,0 +1,58 @@
+#!/bin/bash
+# usage bash prepare.sh MODE
+# FILENAME=$1
+# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer']
+MODE=$1
+
+# dataline=$(cat ${FILENAME})
+
+# parser params
+IFS=$'\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 ${tmp}
+}
+IFS=$'\n'
+# The training params
+model_name=$(func_parser_value "${lines[1]}")
+
+trainer_list=$(func_parser_value "${lines[14]}")
+
+# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer']
+if [ ${MODE} = "lite_train_infer" ];then
+    # pretrain lite train data
+    wget -nc -P  ./pretrain_models/ https://paddlespeech.bj.bcebos.com/Parakeet/speedyspeech_baker_ckpt_0.4.zip
+    wget -nc -P  ./pretrain_models/ https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip
+    (cd ./pretrain_models && unzip speedyspeech_baker_ckpt_0.4.zip && unzip pwg_baker_ckpt_0.4.zip)
+    # generate a config patch
+    echo 'max_epoch: 30' > lite_train_infer.yaml
+    # download data
+    rm -rf ./train_data/mini_BZNSYP
+    wget -nc -P ./train_data/ https://paddlespeech.bj.bcebos.com/datasets/CE/speedyspeech/mini_BZNSYP.tar.gz
+    cd ./train_data/ && tar xzf mini_BZNSYP.tar.gz
+    cd ../
+elif [ ${MODE} = "whole_train_infer" ];then
+    wget -nc -P  ./pretrain_models/ https://paddlespeech.bj.bcebos.com/Parakeet/speedyspeech_baker_ckpt_0.4.zip
+    wget -nc -P  ./pretrain_models/ https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip
+    (cd ./pretrain_models && unzip speedyspeech_baker_ckpt_0.4.zip && unzip pwg_baker_ckpt_0.4.zip)
+    rm -rf ./train_data/processed_BZNSYP
+    wget -nc -P ./train_data/ https://paddlespeech.bj.bcebos.com/datasets/CE/speedyspeech/processed_BZNSYP.tar.gz
+    cd ./train_data/ && tar xzf processed_BZNSYP.tar.gz
+    ln -s ./dump ./BZNSYP
+    cd ../
+else
+    # whole infer using paddle inference library
+    wget -nc -P  ./pretrain_models/ https://paddlespeech.bj.bcebos.com/Parakeet/speedyspeech_pwg_inference_0.4.zip
+    (cd ./pretrain_models && unzip speedyspeech_pwg_inference_0.4.zip)
+fi

tests/speedyspeech_params.txt

@@ -0,0 +1,51 @@
+===========================train_params===========================
+model_name:speedyspeech
+python:python3.7
+gpu_list:0|0,1
+null:null
+null:null
+null:null
+null:null
+null:null
+null:null
+null:null
+null:null
+null:null
+##
+trainer:lite_train|norm_train
+norm_train:../examples/speedyspeech/baker/train.py --train-metadata=train_data/BZNSYP/train/norm/metadata.jsonl --dev-metadata=train_data/BZNSYP/dev/norm/metadata.jsonl --output-dir=exp/lite --nprocs=1
+null:null
+null:null
+null:null
+lite_train:../examples/speedyspeech/baker/train.py --train-metadata=train_data/mini_BZNSYP/train/norm/metadata.jsonl --dev-metadata=train_data/mini_BZNSYP/dev/norm/metadata.jsonl --config=lite_train_infer.yaml --output-dir=exp/default --nprocs=1
+null:null
+##
+===========================eval_params===========================
+eval:../examples/speedyspeech/baker/synthesize_e2e.py --speedyspeech-config=../examples/speedyspeech/baker/conf/default.yaml --speedyspeech-checkpoint=pretrain_models/speedyspeech_baker_ckpt_0.4/speedyspeech_snapshot_iter_91800.pdz --speedyspeech-stat=pretrain_models/speedyspeech_baker_ckpt_0.4/speedy_speech_stats.npy --pwg-config=../examples/parallelwave_gan/baker/conf/default.yaml --pwg-checkpoint=pretrain_models/pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz --pwg-stat=pretrain_models/pwg_baker_ckpt_0.4/pwg_stats.npy --text=../examples/speedyspeech/baker/sentences.txt --output-dir=e2e --inference-dir=inference --device="gpu"
+null:null
+##
+===========================infer_params===========================
+null:null
+null:null
+null:null
+null:null
+null:null
+null:null
+null:null
+null:null
+##
+null:null
+null:null
+null:null
+inference:../examples/speedyspeech/baker/inference.py --inference-dir=pretrain_models/speedyspeech_pwg_inference_0.4 --text=../examples/speedyspeech/baker/sentences.txt --output-dir=inference_out --enable-auto-log
+--use_gpu:True
+null:null
+null:null
+null:null
+null:null
+null:null
+null:null
+null:null
+null:null
+null:null
+null:null

tests/test.sh

@@ -0,0 +1,364 @@
+#!/bin/bash
+# usage: bash test.sh ***.txt MODE
+
+FILENAME=$1
+# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer']
+MODE=$2
+
+dataline=$(cat ${FILENAME})
+
+# parser params
+IFS=$'\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]}")
+
+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}  > ${_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} > ${_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" ]; 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 [ ${infer_run_exports[Count]} != "null" ];then
+            set_export_weight=$(func_set_params "${export_weight}" "${infer_model}")
+            set_save_infer_key=$(func_set_params "${save_infer_key}" "${infer_model}")
+            export_cmd="${python} ${norm_export} ${set_export_weight} ${set_save_infer_key}"
+            eval $export_cmd
+            status_export=$?
+            if [ ${status_export} = 0 ];then
+                status_check $status_export "${export_cmd}" "${status_log}"
+            fi
+        fi
+        #run inference
+        is_quant=${infer_quant_flag[Count]}
+        func_inference "${python}" "${inference_py}" "${infer_model}" "${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} "
+                elif [ ${#gpu} -le 15 ];then  # train with multi-gpu
+                    cmd="${python} ${run_train} --nprocs={#ngpu}"
+                else     # train with multi-machine
+                    cmd="${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