diff --git a/PyTorch/contrib/cv/pose_estimation/VoxelPose/README.md b/PyTorch/contrib/cv/pose_estimation/VoxelPose/README.md
index 4974cf07a4a0235780351f2250b8cd965441a792..fdd1a2623a325b5eac2015f7c4e0b6120b1bb4dd 100644
--- a/PyTorch/contrib/cv/pose_estimation/VoxelPose/README.md
+++ b/PyTorch/contrib/cv/pose_estimation/VoxelPose/README.md
@@ -18,15 +18,10 @@ Set AscendProject = /usr/local/Ascend #use real path
 
 ### Training
 To train a model, run the following scripts with the real path of dataset:
+PS：There is a large jitter on single card training with bs=1, do not perform single card trainning.
 
 ```bash
 # real_data_path = data/shelf
-# training 1p accuracy
-bash ./test/train_full_1p.sh --data_path=real_data_path --device_id=xxx
-
-# training 1p performance
-bash ./test/train_performance_1p.sh --data_path=real_data_path --device_id=xxx
-
 # training 8p accuracy
 bash ./test/train_full_8p.sh --data_path=real_data_path
 
@@ -43,8 +38,8 @@ Log path:
 
 
 ### VoxelPose training result
+说明：由于模型单卡训练（bs=1）抖动大，loss不收敛，暂不持支单卡训练。
 
 | 名称    | 精度       | FPS | AMP_Type   |
 | :------: | :------:  | :------: | :------: |
-| NPU-1p        |  96.62      |   0.289      |   O1      |
 |  NPU-8p  |   97.10    |    1.267     |   O1    |
diff --git a/PyTorch/contrib/cv/pose_estimation/VoxelPose/test/train_full_1p.sh b/PyTorch/contrib/cv/pose_estimation/VoxelPose/test/train_full_1p.sh
deleted file mode 100644
index f9fb187754efe808d752e6bf107102883b19f5b2..0000000000000000000000000000000000000000
--- a/PyTorch/contrib/cv/pose_estimation/VoxelPose/test/train_full_1p.sh
+++ /dev/null
@@ -1,150 +0,0 @@
-# data_path="data/Shelf"
-#
-# for para in $*
-# do
-#     if [[ $para == --data_path* ]]; then
-#         data_path=`echo ${para#*=}`
-#     fi
-# done
-#
-# echo ${data_path}
-#
-# python run/train_3d.py \
-# 	--cfg configs/shelf/prn64_cpn80x80x20.yaml \
-# 	--data_path ${data_path} \
-# 	--num_epochs 1 \
-# 	--apex
-
-#!/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="VoxelPose"
-# 训练batch_size
-batch_size=1
-# 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
-data_path=""
-
-# 训练epoch
-num_epochs=30
-# 指定训练所使用的npu device卡id
-device_id=0
-
-
-# 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 == --device_id* ]];then
-        device_id=`echo ${para#*=}`
-    elif [[ $para == --data_path* ]];then
-        data_path=`echo ${para#*=}`
-    fi
-done
-
-# 校验是否传入data_path,不需要修改
-if [[ $data_path == "" ]];then
-    echo "[Error] para \"data_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
-python3.7 run/train_3d.py \
-    --cfg configs/shelf/prn64_cpn80x80x20.yaml \
-    --data_path ${data_path} \
-    --addr=$(hostname -I |awk '{print $1}') \
-    --num_epochs ${num_epochs} \
-    --apex > ${test_path_dir}/output/${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@all' ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk -F "FPS@all " '{print $2}'| awk -F "," '{print $1}'| awk 'END {print}'`
-#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 'model best precision is'  ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|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`
-CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
-
-##获取性能数据，不需要修改
-#吞吐量
-ActualFPS=${FPS}
-#单迭代训练时长
-TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'*1000/'${FPS}'}'`
-
-#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
-grep -a 'Loss' ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk -F "(" '{print $4}'| 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}'  ${test_path_dir}/output/$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 "ActualLoss = ${ActualLoss}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "E2ETrainingTime = ${e2e_time}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
diff --git a/PyTorch/contrib/cv/pose_estimation/VoxelPose/test/train_performance_1p.sh b/PyTorch/contrib/cv/pose_estimation/VoxelPose/test/train_performance_1p.sh
deleted file mode 100644
index 325a58e88a7f6e98fc098b3c45424bb2906bda89..0000000000000000000000000000000000000000
--- a/PyTorch/contrib/cv/pose_estimation/VoxelPose/test/train_performance_1p.sh
+++ /dev/null
@@ -1,151 +0,0 @@
-# data_path="data/Shelf"
-#
-# for para in $*
-# do
-#     if [[ $para == --data_path* ]]; then
-#         data_path=`echo ${para#*=}`
-#     fi
-# done
-#
-# echo ${data_path}
-#
-# python run/train_3d.py \
-# 	--cfg configs/shelf/prn64_cpn80x80x20.yaml \
-# 	--data_path ${data_path} \
-# 	--num_epochs 1 \
-# 	--apex
-
-#!/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="VoxelPose"
-# 训练batch_size
-batch_size=1
-# 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
-data_path=""
-
-# 训练epoch
-num_epochs=1
-# 指定训练所使用的npu device卡id
-device_id=0
-
-
-# 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 == --device_id* ]];then
-        device_id=`echo ${para#*=}`
-    elif [[ $para == --data_path* ]];then
-        data_path=`echo ${para#*=}`
-    fi
-done
-
-# 校验是否传入data_path,不需要修改
-if [[ $data_path == "" ]];then
-    echo "[Error] para \"data_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
-python3.7 run/train_3d.py \
-    --cfg configs/shelf/prn64_cpn80x80x20.yaml \
-    --data_path ${data_path} \
-    --addr=$(hostname -I |awk '{print $1}') \
-    --num_epochs ${num_epochs} \
-    --apex > ${test_path_dir}/output/${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@all' ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk -F "FPS@all " '{print $2}'| awk -F "," '{print $1}'| awk 'END {print}'`
-#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 'model best precision is'  ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|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`
-CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
-
-##获取性能数据，不需要修改
-#吞吐量
-ActualFPS=${FPS}
-#单迭代训练时长
-TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'*1000/'${FPS}'}'`
-
-#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
-grep -a 'Loss' ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk -F "(" '{print $4}'| 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}'  ${test_path_dir}/output/$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 "ActualLoss = ${ActualLoss}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "E2ETrainingTime = ${e2e_time}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log