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retrieval_task.sh
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#!/bin/sh
<<COMMENT
这个脚本是把训练，生成测试文件，生成 avs检索 结果三部分结合起来，自动进行。
注意：
1. 更改模型在 config 文件中更改。
2. random_seed 是传入 do_train.py 文件的一个参数，如果没有使用可以修改或者删除。
3. result_file 是存储结果的文件名。
3. parm_adjust_configs 是传入 config 文件的一个参数，如果没有使用可以修改或者删除。
COMMENT
# *************************萌萌哒******************************
# 并行参数
Nproc=1 # 可同时运行的最大作业数
devices=()
# shellcheck disable=SC2113
function PushQue { # 将PID压入队列
Que="$Que $1"
Nrun=$(($Nrun+1))
}
function GenQue { # 更新队列
OldQue=$Que
Que=""; Nrun=0
for PID in $OldQue; do
if [[ -d /proc/$PID ]]; then
PushQue $PID
fi
done
}
function ChkQue { # 检查队列
OldQue=$Que
for PID in $OldQue; do
if [[ ! -d /proc/$PID ]] ; then
GenQue; break
fi
done
}
function paralle {
PID=$!
PushQue $PID
while [[ $Nrun -ge ${Nproc} ]]; do
ChkQue
sleep 1
done
}
# *************************萌萌哒******************************
path_shell=`pwd`
cd ..
path_w2vvpp=`pwd`
rootpath="$HOME/hf_code/VisualSearch"
trainCollection=""
valCollection=""
val_set='' # setA
trainCollection2='None'
testCollection=''
txt_feature_task2='no'
config=''
batch_size=128
workers=2
overwrite=0
pretrained_file_path='None' # 默认没有 pretrained_file
num_epochs=80
random_seeds=(2) # 初始化随机数种子
save_mean_last=0
#parm_adjust_configs=()
model_prefix_="runs_"
result_file="$path_w2vvpp/result_log/result_${model_prefix_}_${config}.txt"
# 读取输入的参数
GETOPT_ARGS=$(getopt -o l: -al rootpath:,trainCollection:,valCollection:,val_set:,\
testCollection:,config:,batch_size:,overwrite:,devices:,Nproc:,random_seeds:,parm_adjust_configs:,\
model_prefix_:,result_file:,trainCollection2:,save_mean_last:,num_epochs: -- "$@") # , 后一定不要有空格
eval set -- "$GETOPT_ARGS"
#获取参数
while [ -n "$1" ]
do
case "$1" in
--rootpath) rootpath=$2; shift 2;;
--trainCollection) trainCollection=$2; shift 2;;
--valCollection) valCollection=$2; shift 2;;
--val_set) val_set=$2; shift 2;;
--testCollection) testCollection=$2; shift 2;;
--config) config=$2; shift 2;;
--batch_size) batch_size=$(($2)); shift 2;;
--overwrite) overwrite=$2; shift 2;;
--devices) devices_temp=$2; shift 2;;
--Nproc) Nproc=$(($2)); shift 2;;
--parm_adjust_configs) parm_adjust_configs=$2; shift 2;;
--random_seeds) random_seeds=$2; shift 2;;
--pretrained_file_path) pretrained_file_path=$2; shift 2;;
--model_prefix_) model_prefix_=$2; shift 2;;
--result_file) result_file=$2; shift 2;;
--trainCollection2) trainCollection2=$2; shift 2;;
--save_mean_last) save_mean_last=$2; shift 2;;
--num_epochs) num_epochs=$(($2)); shift 2;;
--) break ;;
*) echo $1,$2; break ;;
esac
done
echo "result_file：$result_file, devices: ${devices[*]} , config: $config , parm_adjust_configs: ${parm_adjust_configs},
Nproc: ${Nproc} "
for each in ${devices_temp[*]}
do
devices[${#devices[@]}]=$each
done
#exit 0
# ****************************************
# 训练
# shellcheck disable=SC2039
device_index=0
for random_seed in ${random_seeds[*]}
do
for parm_adjust_config in ${parm_adjust_configs[*]}
do
model_prefix="${model_prefix_}${parm_adjust_config}_seed_${random_seed}"
device=${devices[device_index]}
let device_index="($device_index + 1) % ${#devices[*]}"
if [[ $Nproc -gt 1 ]]; then
echo "$trainCollection $valCollection --rootpath $rootpath --config $config --val_set $val_set --model_prefix $model_prefix"
python do_trainer.py $trainCollection $valCollection \
--rootpath $rootpath --config_name $config --val_set $val_set --model_prefix $model_prefix \
--batch_size $batch_size --workers $workers --device $device --overwrite $overwrite \
--parm_adjust_config $parm_adjust_config --pretrained_file_path $pretrained_file_path\
--random_seed $random_seed --trainCollection2 $trainCollection2 \
--save_mean_last ${save_mean_last} --num_epochs ${num_epochs} \
& paralle
else
python do_trainer.py $trainCollection $valCollection \
--rootpath $rootpath --config_name $config --val_set $val_set --model_prefix $model_prefix \
--batch_size $batch_size --workers $workers --device $device --overwrite $overwrite \
--parm_adjust_config $parm_adjust_config --pretrained_file_path $pretrained_file_path\
--random_seed $random_seed --trainCollection2 $trainCollection2 \
--save_mean_last ${save_mean_last} --num_epochs ${num_epochs}
fi
done
done
wait
## ******************萌萌哒分割线***********************
# 测试与输出结果
Nproc=1
overwrite=1
batch_size=64
if [[ $trainCollection2 != 'None' ]]
then
trainCollection="${trainCollection}_${trainCollection2}"
fi
for random_seed in ${random_seeds[*]}
do
for parm_adjust_config in ${parm_adjust_configs[*]}
do
model_prefix="${model_prefix_}${parm_adjust_config}_seed_${random_seed}"
if [[ $val_set = 'no' ]]
then
model_path=$rootpath/$trainCollection/w2vvpp_train/$valCollection/$config/$model_prefix/model_best.pth.tar
sim_name=$trainCollection/$valCollection/$config
else
model_path=$rootpath/$trainCollection/w2vvpp_train/$valCollection/$val_set/$config/$model_prefix/model_best.pth.tar
sim_name=$trainCollection/$valCollection/$val_set/$config
fi
query_sets=$testCollection.caption.txt
device=${devices[device_index]}
let device_index="($device_index + 1) % ${#devices[*]}"
if [[ $Nproc -gt 1 ]]; then
python do_predictor.py $testCollection $model_path $sim_name \
--query_sets $query_sets \
--rootpath $rootpath --overwrite $overwrite --device $device \
--batch_size $batch_size --predict_result_file $result_file --num_workers $workers \
& paralle
else
python do_predictor.py $testCollection $model_path $sim_name \
--query_sets $query_sets \
--rootpath $rootpath --overwrite $overwrite --device $device \
--batch_size $batch_size --predict_result_file $result_file --num_workers $workers
fi
done
done
wait
cd $path_shell