#!/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="DCGAN"
# 训练batch_size
batch_size=64
# 训练使用的npu卡数
export RANK_SIZE=1
# 数据集路径,保持为空,不需要修改
checkpoint_path=""

# 训练epoch
train_epochs=20
# 指定训练所使用的npu device卡id
device_id=0
# 加载数据进程数
workers=8
# lr
base_lr=0.0002

# 参数校验，checkpoint_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 == --device_id* ]];then
        device_id=`echo ${para#*=}`
    elif [[ $para == --checkpoint_path* ]];then
        checkpoint_path=`echo ${para#*=}`
    fi
done

# 校验是否传入checkpoint_path,不需要修改
if [[ $checkpoint_path == "" ]];then
    echo "[Error] para \"checkpoint_path\" must be confing"
    exit 1
fi
# 校验是否指定了device_id,分动态分配device_id与手动指定device_id,此处不需要修改
if [ $ASCEND_DEVICE_ID ];then
    echo "device id is ${ASCEND_DEVICE_ID}"
elif [ ${device_id} ];then
    export ASCEND_DEVICE_ID=${device_id}
    echo "device id is ${ASCEND_DEVICE_ID}"
else
    "[Error] device id must be config"
    exit 1
fi



###############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_dirname=${cur_path##*/}
if [ x"${cur_path_last_dirname}" == x"test" ];then
    test_path_dir=${cur_path}
    cd ..
    cur_path=`pwd`
else
    test_path_dir=${cur_path}/test
fi


#################创建日志输出目录，不需要修改#################
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


#################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 ${test_path_dir}/env_npu.sh
fi
python3 ./main.py \
    --data "" \
    --addr=$(hostname -I |awk '{print $1}') \
    --batch-size=${batch_size} \
    --n-epochs ${train_epochs} \
    --lr=${base_lr} \
    --n-cpu ${workers} \
    --world-size=1 \
    --print-freq=1 \
    --gpu=0 \
    --rank 0 \
    --amp \
    --dist-backend 'hccl' \
    --opt-level O2 \
    --device-list '0' \
    --loss-scale 1024 \
    --evaluate \
    --device="npu" \
    --checkpoint-path ${checkpoint_path} \
     > ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &

wait


##################获取训练数据################
#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 'FPS'  ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk -F " " '{print $11}'|awk 'END {print}'`
#打印，不需要修改
echo "Final Performance images/sec : $FPS"
#打印，不需要修改
echo "GAN model : no acc output"
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=${batch_size}
DeviceType=`uname -m`
CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'eval'

#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}'*1000/'${FPS}'}'`

#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 = no batchsize in eval stage" >>  ${test_path_dir}/output/$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 = no acc for dcgan" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualLoss = no Loss in eval stage" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "E2ETrainingTime = ${e2e_time}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log