diff --git a/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/README.md b/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/README.md
index 82b0a668e7ded6f9540dd355b0efb765accdcbaf..2d4c2019298b3232f2e4a0700f83096e660aa0c0 100644
--- a/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/README.md
+++ b/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/README.md
@@ -11,13 +11,13 @@ This folder contains the implementation of the `MoBY` with `Swin Transformer` fo
 - Install `timm==0.3.2`:
 
 ```bash
-pip install timm==0.3.2
+pip3.7 install timm==0.3.2
 ```
 
 - Install other requirements:
 
 ```bash
-pip install opencv-python==4.4.0.46 termcolor==1.1.0 yacs==0.1.8 diffdist
+pip3.7 install opencv-python==4.4.0.46 termcolor==1.1.0 yacs==0.1.8 diffdist
 ```
 
 ### Data preparation
@@ -56,23 +56,9 @@ To train `MoBY` with `Swin Transformer Tiny` on ImageNet, run:
 ```bash
 bash ./test/train_full_8p.sh --data_path=<data_path>
 ```
-
-For example, to train `MoBY` with `Swin Transformer Tiny` with 8 NPU on a single node for 300 epochs, run:
-
-```bash
-bash ./test/train_full_8p.sh --data_path=/data/imagenet
-```
-
 Defaultly, training auto-resumes checkpoint in output directory. Remove the `output` directory to train from begin.
 
 ### Performance Test
-
-To train `MoBY Swin-T` on 1 NPU for performance test, run:
-
-```bash
-bash ./test/train_performance_1p.sh --data_path=<data_path>
-```
-
 For performance test on 8 NPU, run:
 
 ```bash
@@ -87,12 +73,6 @@ To evaluate a pre-trained `MoBY` with `Swin Transformer Tiny` on ImageNet-1K lin
 bash ./test/eval_8p.sh --data_path=<data_path>
 ```
 
-For example, to evaluate `MoBY Swin-T` with 8 NPU on a single node on ImageNet-1K linear evluation, run:
-
-```bash
-bash ./test/eval_8p.sh --data_path=/data/imagenet
-```
-
 ### Training result for `MoBY Swin-T`
 
 | Acc@1    | FPS       | Npu_nums | Epochs   | AMP_Type | CPU |
diff --git a/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/env_npu.sh b/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/env_npu.sh
deleted file mode 100644
index c5f14bc83e77a09f744a6f6bd865b1d948ff5946..0000000000000000000000000000000000000000
--- a/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/env_npu.sh
+++ /dev/null
@@ -1,81 +0,0 @@
-#!/bin/bash
-export install_path=/usr/local/Ascend
-
-if [ -d ${install_path}/toolkit ]; then
-    export LD_LIBRARY_PATH=/usr/include/hdf5/lib/:/usr/local/:/usr/local/lib/:/usr/lib/:${install_path}/fwkacllib/lib64/:${install_path}/driver/lib64/common/:${install_path}/driver/lib64/driver/:${install_path}/add-ons:${path_lib}:${LD_LIBRARY_PATH}
-    export PATH=${install_path}/fwkacllib/ccec_compiler/bin:${install_path}/fwkacllib/bin:$PATH
-    export PYTHONPATH=${install_path}/fwkacllib/python/site-packages:${install_path}/tfplugin/python/site-packages:${install_path}/toolkit/python/site-packages:$PYTHONPATH
-    export PYTHONPATH=/usr/local/python3.7.5/lib/python3.7/site-packages:$PYTHONPATH
-    export ASCEND_OPP_PATH=${install_path}/opp
-else
-    if [ -d ${install_path}/nnae/latest ];then
-        export LD_LIBRARY_PATH=/usr/local/:/usr/local/python3.7.5/lib/:/usr/local/openblas/lib:/usr/local/lib/:/usr/lib64/:/usr/lib/:${install_path}/nnae/latest/fwkacllib/lib64/:${install_path}/driver/lib64/common/:${install_path}/driver/lib64/driver/:${install_path}/add-ons/:/usr/lib/aarch64_64-linux-gnu:$LD_LIBRARY_PATH
-        export PATH=$PATH:${install_path}/nnae/latest/fwkacllib/ccec_compiler/bin/:${install_path}/nnae/latest/toolkit/tools/ide_daemon/bin/
-        export ASCEND_OPP_PATH=${install_path}/nnae/latest/opp/
-        export OPTION_EXEC_EXTERN_PLUGIN_PATH=${install_path}/nnae/latest/fwkacllib/lib64/plugin/opskernel/libfe.so:${install_path}/nnae/latest/fwkacllib/lib64/plugin/opskernel/libaicpu_engine.so:${install_path}/nnae/latest/fwkacllib/lib64/plugin/opskernel/libge_local_engine.so
-        export PYTHONPATH=${install_path}/nnae/latest/fwkacllib/python/site-packages/:${install_path}/nnae/latest/fwkacllib/python/site-packages/auto_tune.egg/auto_tune:${install_path}/nnae/latest/fwkacllib/python/site-packages/schedule_search.egg:$PYTHONPATH
-        export ASCEND_AICPU_PATH=${install_path}/nnae/latest
-    else
-        export LD_LIBRARY_PATH=/usr/local/:/usr/local/lib/:/usr/lib64/:/usr/lib/:/usr/local/python3.7.5/lib/:/usr/local/openblas/lib:${install_path}/ascend-toolkit/latest/fwkacllib/lib64/:${install_path}/driver/lib64/common/:${install_path}/driver/lib64/driver/:${install_path}/add-ons/:/usr/lib/aarch64-linux-gnu:$LD_LIBRARY_PATH
-        export PATH=$PATH:${install_path}/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin/:${install_path}/ascend-toolkit/latest/toolkit/tools/ide_daemon/bin/
-        export ASCEND_OPP_PATH=${install_path}/ascend-toolkit/latest/opp/
-        export OPTION_EXEC_EXTERN_PLUGIN_PATH=${install_path}/ascend-toolkit/latest/fwkacllib/lib64/plugin/opskernel/libfe.so:${install_path}/ascend-toolkit/latest/fwkacllib/lib64/plugin/opskernel/libaicpu_engine.so:${install_path}/ascend-toolkit/latest/fwkacllib/lib64/plugin/opskernel/libge_local_engine.so
-        export PYTHONPATH=${install_path}/ascend-toolkit/latest/fwkacllib/python/site-packages/:${install_path}/ascend-toolkit/latest/fwkacllib/python/site-packages/auto_tune.egg/auto_tune:${install_path}/ascend-toolkit/latest/fwkacllib/python/site-packages/schedule_search.egg:$PYTHONPATH
-        export ASCEND_AICPU_PATH=${install_path}/ascend-toolkit/latest
-    fi
-fi
-
-
-#将Host日志输出到串口,0-关闭/1-开启
-export ASCEND_SLOG_PRINT_TO_STDOUT=0
-#设置默认日志级别,0-debug/1-info/2-warning/3-error
-export ASCEND_GLOBAL_LOG_LEVEL=3
-#设置Event日志开启标志,0-关闭/1-开启
-export ASCEND_GLOBAL_EVENT_ENABLE=0
-#设置是否开启taskque,0-关闭/1-开启
-export TASK_QUEUE_ENABLE=1
-#设置是否开启PTCopy,0-关闭/1-开启
-export PTCOPY_ENABLE=1
-#设置是否开启combined标志,0-关闭/1-开启
-export COMBINED_ENABLE=1
-#设置特殊场景是否需要重新编译,不需要修改
-export DYNAMIC_OP="ADD#MUL"
-#HCCL白名单开关,1-关闭/0-开启
-export HCCL_WHITELIST_DISABLE=1
-export HCCL_IF_IP=$(hostname -I |awk '{print $1}')
-
-export SCALAR_TO_HOST_MEM=1
-export BMMV2_ENABLE=1
-
-#设置device侧日志登记为error
-${install_path}/driver/tools/msnpureport -g error -d 0
-${install_path}/driver/tools/msnpureport -g error -d 1
-${install_path}/driver/tools/msnpureport -g error -d 2
-${install_path}/driver/tools/msnpureport -g error -d 3
-${install_path}/driver/tools/msnpureport -g error -d 4
-${install_path}/driver/tools/msnpureport -g error -d 5
-${install_path}/driver/tools/msnpureport -g error -d 6
-${install_path}/driver/tools/msnpureport -g error -d 7
-#关闭Device侧Event日志
-${install_path}/driver/tools/msnpureport -e disable
-
-path_lib=$(python3.7 -c """
-import sys
-import re
-result=''
-for index in range(len(sys.path)):
-    match_sit = re.search('-packages', sys.path[index])
-    if match_sit is not None:
-        match_lib = re.search('lib', sys.path[index])
-
-        if match_lib is not None:
-            end=match_lib.span()[1]
-            result += sys.path[index][0:end] + ':'
-
-        result+=sys.path[index] + '/torch/lib:'
-print(result)"""
-)
-
-echo ${path_lib}
-
-export LD_LIBRARY_PATH=/usr/local/python3.7.5/lib/:${path_lib}:$LD_LIBRARY_PATH
diff --git a/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/run8p_linear_evaluation.sh b/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/run8p_linear_evaluation.sh
deleted file mode 100644
index cd8463dbccf52047dc5fffe351036f7aec444ec1..0000000000000000000000000000000000000000
--- a/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/run8p_linear_evaluation.sh
+++ /dev/null
@@ -1,2 +0,0 @@
-python -m torch.distributed.launch --nproc_per_node 8 --master_port 12345 moby_linear.py \
---cfg configs/moby_swin_tiny.yaml --data-path /data/imagenet > linear.log 2>&1 &
\ No newline at end of file
diff --git a/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/run8p_pretrain.sh b/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/run8p_pretrain.sh
deleted file mode 100644
index fe2a96ba78ce425f58241229ad6ccb32066736e9..0000000000000000000000000000000000000000
--- a/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/run8p_pretrain.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-source env_npu.sh
-
-export WORLD_SIZE=8
-for i in $(seq 0 7)
-do
-    export RANK=$i
-    start=$((24 * i))
-    end=$((start + 23))
-    taskset -c $start-$end nohup python -u moby_main.py \
-            --cfg configs/moby_swin_tiny.yaml \
-            --data-path /data/imagenet \
-            --local_rank $i \
-            --batch-size 128 > train_${i}.log 2>&1 &
-done
\ No newline at end of file
diff --git a/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/test/eval_8p.sh b/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/test/eval_8p.sh
index ef8490219f0a230f7d53223b80bd9793bd4db603..0c4f6dad0beb48f6db7ce209f92a716bd528ec41 100644
--- a/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/test/eval_8p.sh
+++ b/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/test/eval_8p.sh
@@ -37,11 +37,16 @@ if [[ $data_path == "" ]];then
     exit 1
 fi
 
-# 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 ${cur_path}/env_npu.sh
+##################指定训练脚本执行路径##################
+# cd到与test文件同层级目录下执行脚本，提高兼容性；test_path_dir为包含test文件夹的路径
+cur_path=`pwd`
+cur_path_last_dirname=${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
 
 #进入训练脚本目录，需要模型审视修改
@@ -55,14 +60,28 @@ export RANK_ID=$RANK_ID
 export ASCEND_DEVICE_ID=$RANK_ID
 ASCEND_DEVICE_ID=$RANK_ID
 
+#创建DeviceID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
+
 #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
 
 #执行训练脚本，以下传参不需要修改，其他需要模型审视修改
 export RANK_SIZE=8
 
-python3.7 -m torch.distributed.launch --nproc_per_node 8 --master_port 12345 moby_linear.py \
---cfg configs/moby_swin_tiny.yaml --data-path ${data_path} > ${cur_path}/output/${ASCEND_DEVICE_ID}/eval_${ASCEND_DEVICE_ID}.log 2>&1 &
+nohup python3.7 -m torch.distributed.launch --nproc_per_node 8 --master_port 12345 moby_linear.py \
+--cfg configs/moby_swin_tiny.yaml --data-path ${data_path} > ${test_path_dir}/output/${ASCEND_DEVICE_ID}/eval_${ASCEND_DEVICE_ID}.log 2>&1 &
 
 wait
 
@@ -76,13 +95,13 @@ 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
-time=`grep -a 'time'  $cur_path/output/${ASCEND_DEVICE_ID}/eval_${ASCEND_DEVICE_ID}.log|grep loss|awk -F " " 'END {print $14}'|sed 's/(//g'|sed 's/)//g'`
+time=`grep -a 'time'  ${test_path_dir}/output/${ASCEND_DEVICE_ID}/eval_${ASCEND_DEVICE_ID}.log|grep loss|awk -F " " 'END {print $14}'|sed 's/(//g'|sed 's/)//g'`
 FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${time}'*8}'`
 #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 'Max accuracy' $cur_path/output/${ASCEND_DEVICE_ID}/eval_${ASCEND_DEVICE_ID}.log|awk -F " " 'END {print $8}'|sed 's/%//g'`
+train_accuracy=`grep -a 'Max accuracy' ${test_path_dir}/output/${ASCEND_DEVICE_ID}/eval_${ASCEND_DEVICE_ID}.log|awk -F " " 'END {print $8}'|sed 's/%//g'`
 #打印，不需要修改
 echo "Final Train Accuracy : ${train_accuracy}"
 echo "E2E Training Duration sec : $e2e_time"
@@ -100,21 +119,20 @@ ActualFPS=${FPS}
 TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${time}'}'`
 
 #从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要模型审视修改
-grep 'INFO Train' $cur_path/output/$ASCEND_DEVICE_ID/eval_$ASCEND_DEVICE_ID.log|awk -F "loss " '{print $NF}' | awk -F " " '{print $1}' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
+grep 'INFO Train' ${test_path_dir}/output/$ASCEND_DEVICE_ID/eval_$ASCEND_DEVICE_ID.log|awk -F "loss " '{print $NF}' | awk -F " " '{print $1}' >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
 #最后一个迭代loss值，不需要修改
-ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/eval_${CaseName}_loss.txt|sed 's/.$//'`
+ActualLoss=`awk 'END {print}' ${test_path_dir}/output/$ASCEND_DEVICE_ID/eval_${CaseName}_loss.txt|sed 's/.$//'`
 
 #关键信息打印到${CaseName}.log中，不需要修改
-echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+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 "ActualLoss = ${ActualLoss}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2e_time}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
 #退出anaconda环境
 if [ -n "$conda_name" ];then
     echo "conda $conda_name deactivate"
diff --git a/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/test/train_full_8p.sh b/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/test/train_full_8p.sh
index 213bc33037318d73bd6f11a741704d8a0d163c00..7deb0770c9c8563e697acd27d720c224efdceb6e 100644
--- a/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/test/train_full_8p.sh
+++ b/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/test/train_full_8p.sh
@@ -36,12 +36,22 @@ if [[ $data_path == "" ]];then
     echo "[Error] para \"data_path\" must be confing"
     exit 1
 fi
-
+##################指定训练脚本执行路径##################
+# cd到与test文件同层级目录下执行脚本，提高兼容性；test_path_dir为包含test文件夹的路径
+cur_path=`pwd`
+cur_path_last_dirname=${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
 # 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 ${cur_path}/env_npu.sh
+    source ${test_path_dir}/env_npu.sh
 fi
 
 #进入训练脚本目录，需要模型审视修改
@@ -56,13 +66,14 @@ export ASCEND_DEVICE_ID=$RANK_ID
 ASCEND_DEVICE_ID=$RANK_ID
 
 #创建DeviceID输出目录，不需要修改
-if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then
-    rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID}
-    mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/
+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 ${cur_path}/output/$ASCEND_DEVICE_ID/
+    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID/
 fi
 
+
 #训练开始时间，不需要修改
 start_time=$(date +%s)
 
@@ -79,17 +90,17 @@ do
     then
         PID_START=$((KERNEL_NUM * RANK_ID))
         PID_END=$((PID_START + KERNEL_NUM - 1))
-        taskset -c $PID_START-$PID_END python3.7 -u moby_main.py \
+        taskset -c $PID_START-$PID_END nohup python3.7 -u moby_main.py \
             --cfg configs/moby_swin_tiny.yaml \
             --data-path $data_path \
             --local_rank $RANK_ID \
-            --batch-size $batch_size > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
+            --batch-size $batch_size > ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
     else
-        python3.7 -u moby_main.py \
+        nohup python3.7 -u moby_main.py \
             --cfg configs/moby_swin_tiny.yaml \
             --data-path $data_path \
             --local_rank $RANK_ID \
-            --batch-size $batch_size > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
+            --batch-size $batch_size > ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
     fi
 done
 
@@ -105,13 +116,13 @@ 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
-time=`grep -a 'time'  $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|grep loss|awk -F " " 'END {print $14}'|sed 's/(//g'|sed 's/)//g'`
+time=`grep -a 'time'  ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|grep loss|awk -F " " 'END {print $14}'|sed 's/(//g'|sed 's/)//g'`
 FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${time}'*8}'`
 #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 'Precision' $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk -F " " '{print $NF}'|head -1`
+train_accuracy=`grep -a 'Precision' ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk -F " " '{print $NF}'|head -1`
 #打印，不需要修改
 echo "Final Train Accuracy : ${train_accuracy}"
 echo "E2E Training Duration sec : $e2e_time"
@@ -129,21 +140,21 @@ ActualFPS=${FPS}
 TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${time}'}'`
 
 #从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要模型审视修改
-grep 'INFO Train' $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk -F "loss " '{print $NF}' | awk -F " " '{print $1}' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
+grep 'INFO Train' ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk -F "loss " '{print $NF}' | awk -F " " '{print $1}' >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
 #最后一个迭代loss值，不需要修改
-ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt|sed 's/.$//'`
+ActualLoss=`awk 'END {print}' ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt|sed 's/.$//'`
 
 #关键信息打印到${CaseName}.log中，不需要修改
-echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+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/${CaseName}.log
 #退出anaconda环境
 if [ -n "$conda_name" ];then
     echo "conda $conda_name deactivate"
diff --git a/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/test/train_performance_1p.sh b/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/test/train_performance_1p.sh
index 856cc6eec23b372a8d5c314d4306e361796af3ef..17c6a4565264871364cecdc975f73e8ccd63d360 100644
--- a/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/test/train_performance_1p.sh
+++ b/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/test/train_performance_1p.sh
@@ -2,6 +2,8 @@
 
 #当前路径,不需要修改
 cur_path=`pwd`
+# 指定训练所使用的npu device卡id
+device_id=0
 
 #集合通信参数,不需要修改
 export RANK_SIZE=1
@@ -35,34 +37,53 @@ if [[ $data_path == "" ]];then
     echo "[Error] para \"data_path\" must be confing"
     exit 1
 fi
-
-# 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 ${cur_path}/env_npu.sh
+# 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 ${ASCEND_DEVICE_ID}"
+else
+    "[Error] device id must be config"
+    exit 1
+fi
+#进入训练脚本目录，需要模型审视修改
+cur_path=`pwd`
+cur_path_last_dirname=${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
 
+
 #进入训练脚本目录，需要模型审视修改
 ASCEND_DEVICE_ID=0
 
 #创建DeviceID输出目录，不需要修改
-if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then
-    rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID}
-    mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/
+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 ${cur_path}/output/$ASCEND_DEVICE_ID/
+    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID/
 fi
 
 #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
 #执行训练脚本，以下传参不需要修改，其他需要模型审视修改
-python3.7 -m torch.distributed.launch --nproc_per_node 1 --master_port 12345  moby_main.py \
+nohup python3.7 -m torch.distributed.launch --nproc_per_node 1 --master_port 12345  moby_main.py \
     --cfg configs/moby_swin_tiny.yaml \
     --data-path $data_path \
     --steps 1000 \
-    --batch-size 128 > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
+    --batch-size 128 > ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
 
 wait
 
@@ -73,7 +94,7 @@ 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
-time=`grep -a 'time'  $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|grep loss|awk -F " " 'END {print $13}'`
+time=`grep -a 'time'  ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|grep loss|awk -F " " 'END {print $13}'`
 FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${time}'}'`
 #打印，不需要修改
 echo "Final Performance images/sec : $FPS"
@@ -94,21 +115,21 @@ ActualFPS=${FPS}
 TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${time}'}'`
 
 #从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要模型审视修改
-grep 'INFO Train' $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk -F "loss " '{print $NF}' | awk -F " " '{print $1}' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
+grep 'INFO Train' ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk -F "loss " '{print $NF}' | awk -F " " '{print $1}' >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
 
 #最后一个迭代loss值，不需要修改
-ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`
+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}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+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 "ActualLoss = ${ActualLoss}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2e_time}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
 #退出anaconda环境
 if [ -n "$conda_name" ];then
     echo "conda $conda_name deactivate"
diff --git a/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/test/train_performance_8p.sh b/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/test/train_performance_8p.sh
index 0e17d260314c6c24593c50aa3d997ce4fac856b0..5192ea1ac4d1fc690e3ddd8603ee37b060e8fda5 100644
--- a/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/test/train_performance_8p.sh
+++ b/PyTorch/built-in/cv/classification/Transformer-SSL_for_PyTorch/test/train_performance_8p.sh
@@ -36,16 +36,38 @@ if [[ $data_path == "" ]];then
     echo "[Error] para \"data_path\" must be confing"
     exit 1
 fi
+##################指定训练脚本执行路径##################
+# cd到与test文件同层级目录下执行脚本，提高兼容性；test_path_dir为包含test文件夹的路径
+cur_path=`pwd`
+cur_path_last_dirname=${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
 
+
+#进入训练脚本目录，需要模型审视修改
+cd $cur_path/
+
+#训练开始时间，不需要修改
+start_time=$(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 ${cur_path}/env_npu.sh
+    source ${test_path_dir}/env_npu.sh
 fi
 
-#进入训练脚本目录，需要模型审视修改
-cd $cur_path/
+#创建DeviceID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
 
 #设置环境变量，不需要修改
 RANK_ID=0
@@ -55,18 +77,6 @@ export RANK_ID=$RANK_ID
 export ASCEND_DEVICE_ID=$RANK_ID
 ASCEND_DEVICE_ID=$RANK_ID
 
-#创建DeviceID输出目录，不需要修改
-if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then
-    rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID}
-    mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/
-else
-    mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/
-fi
-
-
-#训练开始时间，不需要修改
-start_time=$(date +%s)
-
 #执行训练脚本，以下传参不需要修改，其他需要模型审视修改
 export RANK_SIZE=8
 
@@ -81,19 +91,19 @@ do
     then
         PID_START=$((KERNEL_NUM * RANK_ID))
         PID_END=$((PID_START + KERNEL_NUM - 1))
-        taskset -c $PID_START-$PID_END python3.7 -u moby_main.py \
+        taskset -c $PID_START-$PID_END nohup python3.7 -u moby_main.py \
             --cfg configs/moby_swin_tiny.yaml \
             --data-path $data_path \
             --epochs 2 \
             --local_rank $RANK_ID \
-            --batch-size $batch_size > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
+            --batch-size $batch_size > ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
     else
-        python3.7 -u moby_main.py \
+        nohup python3.7 -u moby_main.py \
             --cfg configs/moby_swin_tiny.yaml \
             --data-path $data_path \
             --epochs 2 \
             --local_rank $RANK_ID \
-            --batch-size $batch_size > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
+            --batch-size $batch_size > ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
     fi
 done
 
@@ -109,7 +119,7 @@ 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
-time=`grep -a 'time'  $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|grep loss|awk -F " " 'END {print $14}'|sed 's/(//g'|sed 's/)//g'`
+time=`grep -a 'time'  ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|grep loss|awk -F " " 'END {print $14}'|sed 's/(//g'|sed 's/)//g'`
 FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${time}'*8}'`
 #打印，不需要修改
 echo "Final Performance images/sec : $FPS"
@@ -130,21 +140,21 @@ ActualFPS=${FPS}
 TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${time}'}'`
 
 #从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要模型审视修改
-grep 'INFO Train' $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk -F "loss " '{print $NF}' | awk -F " " '{print $1}' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
+grep 'INFO Train' ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk -F "loss " '{print $NF}' | awk -F " " '{print $1}' >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
 
 #最后一个迭代loss值，不需要修改
-ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`
+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}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+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 "ActualLoss = ${ActualLoss}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2e_time}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
 #退出anaconda环境
 if [ -n "$conda_name" ];then
     echo "conda $conda_name deactivate"