#!/bin/bash

################基础配置参数，需要模型审视修改##################
# 必选字段(必须在此处定义的参数): Network batch_size RANK_SIZE
# 网络名称，同目录名称
Network="MobileNetV3_for_Cifar10"
# 训练batch_size
batch_size=2048
# 训练learning_rate
learning_rate=1.0
# 训练数据数量
NUM_SAMPLES=50000
# 训练使用的npu卡数
export RANK_SIZE=4
export WORLD_SIZE=4

# 参数校验，data_path为必传参数，其他参数的增删由模型自身决定；此处新增参数需在上面有定义并赋值
for para in $*
do
    if [[ $para == --batch_size* ]];then
        batch_size=`echo ${para#*=}`
    elif [[ $para == --learning_rate* ]];then
        learning_rate=`echo ${para#*=}`
    fi
done


###############指定训练脚本执行路径###############
# cd到与test文件夹同层级目录下执行脚本，提高兼容性；test_path_dir为包含test文件夹的路径
cur_path=`pwd`
cur_path_last_diename=${cur_path##*/}
if [ x"${cur_path_last_diename}" == x"test" ];then
    test_path_dir=${cur_path}
    cd ..
    cur_path=`pwd`
else
    test_path_dir=${cur_path}/test
fi


#################启动训练脚本#################
#训练开始时间，不需要修改
start_time=$(date +%s)
# 非平台场景时source 环境变量
check_etp_flag=`env | grep etp_running_flag`
etp_flag=`echo ${check_etp_flag#*=}`
if [ x"${etp_flag}" != x"true" ];then
    source ${test_path_dir}/env_npu.sh
fi


RANK_ID_START=0

export PYTHONPATH=./:$PYTHONPATH

for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++))
do
    echo ${RANK_ID}
    ASCEND_DEVICE_ID=${RANK_ID}
    KERNEL_NUM=$(($(nproc)/8))
	PID_START=$((KERNEL_NUM * RANK_ID))
	PID_END=$((PID_START + KERNEL_NUM - 1))
    if [ -d ${test_path_dir}/output/${ASCEND_DEVICE_ID} ];then
        rm -rf ${test_path_dir}/output/${ASCEND_DEVICE_ID}
        mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
    else
        mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
    fi

    nohup taskset -c $PID_START-$PID_END python3.7 -u train.py \
        --data-dir='./data' --batch-size=${batch_size} --mode=small --print-freq=100 \
        --dataset=CIFAR10 --ema-decay=0 --label-smoothing=0 --lr=${learning_rate} \
        --save-epoch-freq=1000 --lr-decay=cos --lr-min=0 --warmup-epochs=5 \
        --weight-decay=6e-5 --num-epochs=400 --num-workers=16 --width-multiplier=1 \
        --local_rank $RANK_ID \
        --world_size $WORLD_SIZE > ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log &

done

wait

##################获取训练数据################

ASCEND_DEVICE_ID=0

#训练结束时间，不需要修改
end_time=$(date +%s)
e2e_time=$(( $end_time - $start_time ))

#结果打印，不需要修改
echo "------------------ Final result ------------------"
#输出性能FPS，需要模型审视修改
FPS=`grep -a 'Train:' ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|tail -n 5|awk -F " " '{print $9}'|awk '{sum+=$1} END {print '${batch_size}'/(sum/NR)}'`
#打印，不需要修改
echo "Final Performance images/sec : $FPS"

#输出训练精度,需要模型审视修改
train_accuracy=`grep -a 'Val:' ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print}'|awk -F "ACC@1:" '{print $NF}'|awk -F " " '{print $5}'`
#打印，不需要修改
echo "Final Train Accuracy : ${train_accuracy}"
echo "E2E Training Duration sec : $e2e_time"

#性能看护结果汇总
#训练用例信息，不需要修改
BatchSize=${batch_size}
DeviceType=`uname -m`

if [[ $precision_mode == "O0" ]];then
    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'fp32'_'acc'
else
    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc'
fi
#获取性能数据，不需要修改
#吞吐量
ActualFPS=${FPS}
#单迭代训练时长
TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'*100/'${FPS}'}'`

#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
grep Train: ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "train_loss:" '{print $NF}' | awk -F " " '{print $1}' >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt

grep Val: ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "val_loss:" '{print $NF}' | awk -F " " '{print $1}' >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/val_${CaseName}_loss.txt

#最后一个迭代loss值，不需要修改
ActualLoss=`awk 'END {print}'  ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`

#关键信息打印到${CaseName}.log中，不需要修改
echo "Network = ${Network}" >  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "RankSize = ${RANK_SIZE}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "BatchSize = ${BatchSize}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "DeviceType = ${DeviceType}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "CaseName = ${CaseName}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualFPS = ${ActualFPS}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainingTime = ${TrainingTime}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainAccuracy = ${train_accuracy}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualLoss = ${ActualLoss}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "E2ETrainingTime = ${e2e_time}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log