#!/bin/bash

experiment_name="biggan"
weights_path=""
# device id
device_id=0

# 参数校验，weights_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 == --weights_path* ]];then
        weights_path=`echo ${para#*=}`
    fi
done

# 校验是否传入weights_path,不需要修改
if [[ weights_path == "" ]];then
    echo "[Error] para \"weights_path\" must be config"
    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

#################启动训练脚本#################
# 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

echo ${weights_path}

python3 -u evaluation.py \
  --weights_path ${weights_path} \
  --G_attn 64 --D_attn 64 \
  --G_nl inplace_relu --D_nl inplace_relu \
  --SN_eps 1e-6 --BN_eps 1e-5 --adam_eps 1e-6 \
  --G_ortho 0.0 \
  --G_shared \
  --G_init ortho --D_init ortho \
  --hier --dim_z 120 --shared_dim 128 \
  --G_eval_mode \
  --G_ch 96 --D_ch 96 \
  --seed 0 \
  --device='npu' \
  --resume \
  --gpu ${device_id} \
  --use_fp16 \
  --opt_level O2