add run.sh

tools/run.sh

+#!/usr/bin/env bash
+GPU_IDS="0,1,2,3"
+
+# Basic Config
+CONFIG="ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0.yaml"
+EPOCHS=1
+OUTPUT="output_debug4"
+STATUS_LOG="${OUTPUT}/status_result.log"
+RESULT="${OUTPUT}/result.log"
+
+
+# Search Options
+LR_LIST=( 0.0075 0.01 0.0125 )
+RESOLUTION_LIST=( 176 192 224 )
+RA_PROB_LIST=( 0.0 0.1 0.5 )
+RE_PROB_LIST=( 0.0 0.1 0.5 )
+LR_MULT_LIST=( [0.0,0.2,0.4,0.6,0.8,1.0] [0.0,0.4,0.4,0.8,0.8,1.0] )
+TEACHER_LIST=( "ResNet101_vd" "ResNet50_vd" )
+
+
+# Train Mode
+declare -A MODE_MAP
+MODE_MAP=(["search_lr"]=1 ["search_resolution"]=1 ["search_ra_prob"]=1 ["search_re_prob"]=1 ["search_lr_mult_list"]=1  ["search_teacher"]=1 ["train_distillation_model"]=1)
+
+export CUDA_VISIBLE_DEVICES=${GPU_IDS}
+
+
+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 ${run_log}
+    fi
+}
+
+
+function get_max_value(){
+    array=($*)
+    max=${array[0]}
+    index=0
+    for (( i=0; i<${#array[*]-1}; i++ )); do
+        if [[ $(echo "${array[$i]} > $max"|bc) -eq 1 ]]; then
+            max=${array[$i]}
+            index=${i}
+        else
+            continue
+        fi
+    done
+    echo ${max}
+    echo ${index}
+}
+
+function get_best_info(){
+    _parameter=$1
+    params_index=2
+    if [[ ${_parameter} == "TEACHER" ]]; then
+        params_index=3
+    fi
+    parameters_list=$(find ${OUTPUT}/${_parameter}* -name train.log  | awk -v params_index=${params_index} -F "/" '{print $params_index}')
+    metric_list=$(find ${OUTPUT}/${_parameter}* -name train.log | xargs cat | grep "best" | grep "Epoch ${EPOCHS}" | awk -F " " '{print substr($NF,0,7)}')
+    best_info=$(get_max_value ${metric_list[*]})
+    best_metric=$(echo $best_info | awk -F " " '{print $1}')
+    best_index=$(echo $best_info | awk -F " " '{print $2}')
+    best_parameter=$(echo $parameters_list | awk -v best=$(($best_index+1))  '{print $best}' | awk -F "_" '{print $2}')
+    echo ${best_metric}
+    echo ${best_parameter}
+}
+
+
+function search_lr(){
+    for lr in ${LR_LIST[*]}; do
+        cmd_train="python3.7 -m paddle.distributed.launch --gpus=${GPU_IDS} tools/train.py \
+                       -c ${CONFIG} \
+                       -o Global.output_dir=${OUTPUT}/LR_${lr} \
+                       -o Optimizer.lr.learning_rate=${lr} \
+                       -o Global.epochs=${EPOCHS}"
+        eval ${cmd_train}
+        status_check $? "${cmd_train}" "${STATUS_LOG}"
+        cmd="find ${OUTPUT} -name epoch* | xargs rm -rf"
+        eval ${cmd}
+    done
+}
+
+
+function search_resolution(){
+    _lr=$1
+    for resolution in ${RESOLUTION_LIST[*]}; do
+        cmd_train="python3.7 -m paddle.distributed.launch --gpus=${GPU_IDS} tools/train.py \
+            -c ${CONFIG} \
+            -o Global.output_dir=${OUTPUT}/RESOLUTION_${resolution} \
+            -o Optimizer.lr.learning_rate=${_lr} \
+            -o Global.epochs=${EPOCHS} \
+            -o DataLoader.Train.dataset.transform_ops.1.RandCropImage.size=${resolution}"
+        eval ${cmd_train}
+        status_check $? "${cmd_train}" "${STATUS_LOG}"
+        cmd="find ${OUTPUT} -name epoch* | xargs rm -rf"
+        eval ${cmd}
+    done
+}
+
+
+
+function search_ra_prob(){
+    _lr=$1
+    _resolution=$2
+    for ra_prob in ${RA_PROB_LIST[*]}; do
+        cmd_train="python3.7 -m paddle.distributed.launch --gpus=${GPU_IDS} tools/train.py \
+            -c ${CONFIG} \
+            -o Global.output_dir=${OUTPUT}/RA_${ra_prob} \
+            -o Optimizer.lr.learning_rate=${_lr} \
+            -o Global.epochs=${EPOCHS} \
+            -o DataLoader.Train.dataset.transform_ops.3.TimmAutoAugment.prob=${ra_prob} \
+            -o DataLoader.Train.dataset.transform_ops.1.RandCropImage.size=${_resolution} \
+            -o DataLoader.Train.dataset.transform_ops.3.TimmAutoAugment.img_size=${_resolution}"
+        eval ${cmd_train}
+        status_check $? "${cmd_train}" "${STATUS_LOG}"
+        cmd="find ${OUTPUT} -name epoch* | xargs rm -rf"
+        eval ${cmd}
+    done
+}
+
+
+
+function search_re_prob(){
+    _lr=$1
+    _resolution=$2
+    _ra_prob=$3
+    for re_prob in ${RE_PROB_LIST[*]}; do
+        cmd_train="python3.7 -m paddle.distributed.launch --gpus=${GPU_IDS} tools/train.py \
+            -c ${CONFIG} \
+            -o Global.output_dir=${OUTPUT}/RE_${re_prob} \
+            -o Optimizer.lr.learning_rate=${_lr} \
+            -o Global.epochs=${EPOCHS} \
+            -o DataLoader.Train.dataset.transform_ops.3.TimmAutoAugment.prob=${_ra_prob} \
+            -o DataLoader.Train.dataset.transform_ops.5.RandomErasing.EPSILON=${re_prob} \
+            -o DataLoader.Train.dataset.transform_ops.1.RandCropImage.size=${_resolution} \
+            -o DataLoader.Train.dataset.transform_ops.3.TimmAutoAugment.img_size=${_resolution}"
+        eval ${cmd_train}
+        status_check $? "${cmd_train}" "${STATUS_LOG}"
+        cmd="find ${OUTPUT} -name epoch* | xargs rm -rf"
+        eval ${cmd}
+    done
+}
+
+
+function search_lr_mult_list(){
+    _lr=$1
+    _resolution=$2
+    _ra_prob=$3
+    _re_prob=$4
+
+    for lr_mult in ${LR_MULT_LIST[*]}; do
+        cmd_train="python3.7 -m paddle.distributed.launch --gpus=${GPU_IDS} tools/train.py \
+            -c ${CONFIG} \
+            -o Global.output_dir=${OUTPUT}/LR_MULT_${lr_mult} \
+            -o Optimizer.lr.learning_rate=${_lr} \
+            -o Global.epochs=${EPOCHS} \
+            -o DataLoader.Train.dataset.transform_ops.3.TimmAutoAugment.prob=${_ra_prob} \
+            -o DataLoader.Train.dataset.transform_ops.5.RandomErasing.EPSILON=${_re_prob} \
+            -o DataLoader.Train.dataset.transform_ops.1.RandCropImage.size=${_resolution} \
+            -o DataLoader.Train.dataset.transform_ops.3.TimmAutoAugment.img_size=${_resolution} \
+            -o Arch.lr_mult_list=${lr_mult}"
+        eval ${cmd_train}
+        status_check $? "${cmd_train}" "${STATUS_LOG}"
+        cmd="find ${OUTPUT} -name epoch* | xargs rm -rf"
+        eval ${cmd}
+    done
+
+}
+
+
+function search_teacher(){
+    _lr=$1
+    _resolution=$2
+    _ra_prob=$3
+    _re_prob=$4
+
+    for teacher in ${TEACHER_LIST[*]}; do
+        cmd_train="python3.7 -m paddle.distributed.launch --gpus=${GPU_IDS} tools/train.py \
+            -c ${CONFIG} \
+            -o Global.output_dir=${OUTPUT}/TEACHER_${teacher} \
+            -o Optimizer.lr.learning_rate=${_lr} \
+            -o Global.epochs=${EPOCHS} \
+            -o DataLoader.Train.dataset.transform_ops.3.TimmAutoAugment.prob=${_ra_prob} \
+            -o DataLoader.Train.dataset.transform_ops.5.RandomErasing.EPSILON=${_re_prob} \
+            -o DataLoader.Train.dataset.transform_ops.1.RandCropImage.size=${_resolution} \
+            -o DataLoader.Train.dataset.transform_ops.3.TimmAutoAugment.img_size=${_resolution} \
+            -o Arch.name=${teacher}"
+        eval ${cmd_train}
+        status_check $? "${cmd_train}" "${STATUS_LOG}"
+        cmd="find ${OUTPUT}/* -name epoch* | xargs rm -rf"
+        eval ${cmd}
+    done
+}
+
+
+# train the model for knowledge distillation
+function train_distillation_model(){
+    _lr=$1
+    _resolution=$2
+    _ra_prob=$3
+    _re_prob=$4
+    _lr_mult=$5
+    teacher=$6
+    t_pretrained_model="${OUTPUT}/TEACHER_${teacher}/${teacher}/best_model"
+    config="ppcls/configs/cls_demo/person/Distillation/PPLCNet_x1_0_distillation.yaml"
+    combined_label_list="./dataset/person/train_list_for_distill.txt"
+
+    cmd_train="python3.7 -m paddle.distributed.launch \
+        --gpus=${GPU_IDS} \
+        tools/train.py -c ${config} \
+        -o Global.output_dir=${OUTPUT}/kd_teacher \
+        -o Optimizer.lr.learning_rate=${_lr} \
+        -o Global.epochs=${EPOCHS} \
+        -o DataLoader.Train.dataset.transform_ops.3.TimmAutoAugment.prob=${_ra_prob} \
+        -o DataLoader.Train.dataset.transform_ops.5.RandomErasing.EPSILON=${_re_prob} \
+        -o DataLoader.Train.dataset.transform_ops.1.RandCropImage.size=${_resolution} \
+        -o DataLoader.Train.dataset.transform_ops.3.TimmAutoAugment.img_size=${_resolution} \
+        -o DataLoader.Train.dataset.cls_label_path=${combined_label_list} \
+        -o Arch.models.0.Teacher.name="${teacher}" \
+        -o Arch.models.0.Teacher.pretrained="${t_pretrained_model}" \
+        -o Arch.models.1.Student.lr_mult_list=${_lr_mult}"
+    eval ${cmd_train}
+    status_check $? "${cmd_train}" "${STATUS_LOG}"
+    cmd="find ${OUTPUT} -name epoch* | xargs rm -rf"
+    eval ${cmd}
+}
+
+######## Train PaddleClas  ########
+rm -rf ${OUTPUT}
+
+# Train and get best lr
+best_lr=0.01
+if [[ ${MODE_MAP["search_lr"]} -eq 1 ]]; then
+    search_lr
+    best_info=$(get_best_info "LR_[0-9]")
+    best_metric=$(echo $best_info | awk -F " " '{print $1}')
+    best_lr=$(echo $best_info | awk -F " " '{print $2}')
+    echo "The best lr is ${best_lr}, and the best metric is ${best_metric}" >> ${RESULT}
+fi
+
+# Train and get best resolution
+best_resolution=192
+if [[ ${MODE_MAP["search_resolution"]} -eq 1 ]]; then
+    search_resolution "${best_lr}"
+    best_info=$(get_best_info "RESOLUTION")
+    best_metric=$(echo $best_info | awk -F " " '{print $1}')
+    best_resolution=$(echo $best_info | awk -F " " '{print $2}')
+    echo "The best resolution is ${best_resolution}, and the best metric is ${best_metric}" >> ${RESULT}
+fi
+
+# Train and get best ra_prob
+best_ra_prob=0.0
+if [[ ${MODE_MAP["search_ra_prob"]} -eq 1 ]]; then
+    search_ra_prob "${best_lr}" "${best_resolution}"
+    best_info=$(get_best_info "RA")
+    best_metric=$(echo $best_info | awk -F " " '{print $1}')
+    best_ra_prob=$(echo $best_info | awk -F " " '{print $2}')
+    echo "The best ra_prob is ${best_ra_prob}, and the best metric is ${best_metric}" >> ${RESULT}
+fi
+
+# Train and get best re_prob
+best_re_prob=0.1
+if [[ ${MODE_MAP["search_re_prob"]} -eq 1 ]]; then
+    search_re_prob "${best_lr}" "${best_resolution}" "${best_ra_prob}"
+    best_info=$(get_best_info "RE")
+    best_metric=$(echo $best_info | awk -F " " '{print $1}')
+    best_re_prob=$(echo $best_info | awk -F " " '{print $2}')
+   echo "The best re_prob is ${best_re_prob}, and the best metric is ${best_metric}" >> ${RESULT}
+fi
+
+# Train and get best lr_mult_list
+best_lr_mult_list=[1.0,1.0,1.0,1.0,1.0,1.0]
+if [[ ${MODE_MAP["search_lr_mult_list"]} -eq 1 ]]; then
+    search_lr_mult_list "${best_lr}" "${best_resolution}" "${best_ra_prob}" "${best_re_prob}"
+    best_info=$(get_best_info "LR_MULT")
+    best_metric=$(echo $best_info | awk -F " " '{print $1}')
+    best_lr_mult_list=$(echo $best_info | awk -F " " '{print $2}')
+    echo "The best lr_mult_list is ${best_lr_mult_list}, and the best metric is ${best_metric}" >> ${RESULT}
+fi
+
+# train and get best teacher
+best_teacher="ResNet101_vd"
+if [[ ${MODE_MAP["search_teacher"]} -eq 1 ]]; then
+    search_teacher "${best_lr}" "${best_resolution}" "${best_ra_prob}" "${best_re_prob}"
+    best_info=$(get_best_info "TEACHER")
+    best_metric=$(echo $best_info | awk -F " " '{print $1}')
+    best_teacher=$(echo $best_info | awk -F " " '{print $2}')
+    echo "The best teacher is ${best_teacher}, and the best metric is ${best_metric}" >> ${RESULT}
+fi
+
+# train the distillation model
+if [[ ${MODE_MAP["train_distillation_model"]} -eq 1 ]]; then
+    train_distillation_model "${best_lr}" "${best_resolution}" "${best_ra_prob}" "${best_re_prob}" "${best_lr_mult_list}" ${best_teacher}
+    best_info=$(get_best_info "kd_teacher/DistillationModel")
+    best_metric=$(echo $best_info | awk -F " " '{print $1}')
+    echo "the distillation best metric is ${best_metric}, it is global best metric!" >> ${RESULT}
+fi
+