diff --git a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/README.md b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/README.md
index 20421f5559d36ecb2eb1d185a6d6d2bf0c9b3dbc..6a50528317ce97a40f7368cc81ac1f8710793969 100644
--- a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/README.md
+++ b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/README.md
@@ -161,6 +161,11 @@ bash train_performance_bs32_8p.sh
 ```
 
 当前需开启混合精度模式训练方式：
+1P training is also similar to the former:
+```
+cd /path/to/ResNet50_ID0058_for_TensorFlow/test
+train_performance_bs256_hw192_1p.sh --bind_core=1 --precision_mode="allow_mix_precision"
+```
 8P training is also similar to the former:
 ```
 cd /path/to/ResNet50_ID0058_for_TensorFlow/test
diff --git a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_full_1p.sh b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_full_1p.sh
index 1f777698282d0e15cc45644e21650ead041c3c70..8b8889bafeae515f3f500a665df2abd1e7b5b4f3 100644
--- a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_full_1p.sh
+++ b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_full_1p.sh
@@ -4,36 +4,33 @@
 cur_path=`pwd`
 
 #集合通信参数,不需要修改
-#保证rank table file 文件rank_table_8p.json存放在和test同级的configs目录下
-export RANK_SIZE=8
-export RANK_TABLE_FILE=${cur_path}/../configs/8p.json
-export JOB_ID=10087
+export RANK_SIZE=1
+export JOB_ID=99990001
+export RANK_ID=1
+export HCCL_CONNECT_TIMEOUT=600
 RANK_ID_START=0
 
 # 数据集路径,保持为空,不需要修改
 data_path=""
 
 #设置默认日志级别,不需要修改
-export ASCEND_GLOBAL_LOG_LEVEL=3
+export ASCEND_GLOBAL_LOG_LEVEL_ETP_ETP_ETP=3
 
-#基础参数 需要模型审视修改
+#基础参数，需要模型审视修改
 #网络名称，同目录名称
-Network="ResNet50_for_TensorFlow"
+Network="ResNet50_ID0058_for_TensorFlow"
+#训练epoch
+#train_epochs=1
+#训练batch_size
+batch_size=256
+#训练step
+#train_steps=56300
+#学习率
+learning_rate=
 
-export HCCL_CONNECT_TIMEOUT=600
-corenum=`cat /proc/cpuinfo |grep "processor"|wc -l`
-export RANK_INDEX=0
-export RANK_ID=0
-
-config_file=res50_256bs_8p
-max_train_steps=1000
-iterations_per_loop=100
-debug=True
-eval=True
 #维测参数，precision_mode需要模型审视修改
 precision_mode="must_keep_origin_dtype"
-
-#维持参数，不需要修改
+#维持参数，以下不需要修改
 over_dump=False
 data_dump_flag=False
 data_dump_step="10"
@@ -42,17 +39,17 @@ autotune=False
 
 # 帮助信息，不需要修改
 if [[ $1 == --help || $1 == -h ]];then
-    echo"usage:./train_full_8p.sh <args>"
+    echo"usage:./train_full_1p.sh <args>"
     echo " "
     echo "parameter explain:
-    --precision_mode           precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision)
-    --over_dump		           if or not over detection, default is False
-    --data_dump_flag		   data dump flag, default is 0
-    --data_dump_step		   data dump step, default is 10
-    --profiling		           if or not profiling for performance debug, default is False
-    --autotune                 whether to enable autotune, default is False
-    --data_path		           source data of training
-    -h/--help		           show help message
+    --precision_mode         precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision)
+    --over_dump		         if or not over detection, default is False
+    --data_dump_flag	     data dump flag, default is False
+    --data_dump_step		 data dump step, default is 10
+    --profiling		         if or not profiling for performance debug, default is False
+    --autotune               whether to enable autotune, default is False
+    --data_path		         source data of training
+    -h/--help		         show help message
     "
     exit 1
 fi
@@ -104,56 +101,38 @@ if [[ $data_path == "" ]];then
     exit 1
 fi
 
-#autotune时，先开启autotune执行单P训练，不需要修改
-if [[ $autotune == True ]]; then
-    train_full_1p.sh --autotune=$autotune --data_path=$data_path
-    wait
-    autotune=False
-fi
+#修改参数
+sed -i "50s|PATH_TO_BE_CONFIGURED|${data_path}|g"  $cur_path/../src/configs/res50_256bs_1p_eval.py
+sed -i "107s|PATH_TO_BE_CONFIGURED|${cur_path}/output/0/d\_solution/ckpt0|g"  $cur_path/../src/configs/res50_256bs_1p_eval.py
 
+cp data_loader.py $cur_path/../src/data_loader/resnet50/
 #训练开始时间，不需要修改
 start_time=$(date +%s)
-
+cd $cur_path/../
 #进入训练脚本目录，需要模型审视修改
-cd $cur_path/..
 for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
 do
     #设置环境变量，不需要修改
-    echo "Device ID: $RANK_ID"
-    # export RANK_ID=$RANK_ID
-    export ASCEND_DEVICE_ID=$RANK_ID
-    ASCEND_DEVICE_ID=$RANK_ID
-    export DEVICE_ID=$ASCEND_DEVICE_ID
-	DEVICE_INDEX=$(( DEVICE_ID + RANK_INDEX * 8 ))
-	export DEVICE_INDEX=$DEVICE_INDEX
-
+    echo "Device ID: $ASCEND_DEVICE_ID"
+    export RANK_ID=$RANK_ID
+    export DEVICE_INDEX=$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
+    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/ckpt
     else
         mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt
     fi
 
-    #执行训练脚本，需要模型审视修改
-	taskset -c $((ASCEND_DEVICE_ID*${corenum}/8))-$(((ASCEND_DEVICE_ID+1)*${corenum}/8-1)) python3.7 ${cur_path}/../src/mains/res50.py \
-	    --config_file=$config_file \
-        --max_train_steps=$max_train_steps \
-	    --iterations_per_loop=$iterations_per_loop \
-	    --debug=$debug \
-	    --eval=$eval \
-	    --model_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt \
-		--over_dump=${over_dump} \
-		--over_dump_path=${over_dump_path} \
-		--precision_mode ${precision_mode} \
-		> ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
-        #--precision_mode=${precision_mode} \
-        #--data_dump_flag=${data_dump_flag} \
-        #--data_dump_step=${data_dump_step} \
-        #--data_dump_path=${data_dump_path} \
-        #--profiling=${profiling} \
-        #--profiling_dump_path=${profiling_dump_path} \
-        #--autotune=${autotune} \
+    #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
+    #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune
+    nohup python3.7 ${cur_path}/../src/mains/res50.py --config_file=res50_256bs_1p_eval \
+    --iterations_per_loop=100 \
+    --debug=True \
+    --eval=True \
+    --precision_mode ${precision_mode} \
+    --model_dir=${cur_path}/output/${ASCEND_DEVICE_ID}/d_solution/ckpt${ASCEND_DEVICE_ID} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
 done 
 wait
 
@@ -161,24 +140,27 @@ wait
 end_time=$(date +%s)
 e2e_time=$(( $end_time - $start_time ))
 
-echo "------------------ Final result ------------------"
-#单step时长，需要从train_$ASCEND_DEVICE_ID.log里，通过关键字获取。需要模型审视修改
-#step_sec=`grep TimeHistory  $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk 'END {print $6}'`
-#echo "Final Performance ms/step : $step_sec"
-#计算训练时长，需要模型审视修改
-#step_sec=`echo ${step_sec%.*}`
-#e2e_sec=`expr ${train_epochs} \* 1281167 / ${step_sec} `
-#echo "Final Training Duration sec : $e2e_sec"
-#训练精度，需要从train_$ASCEND_DEVICE_ID.log里，通过关键字获取。需要模型审视修改
-li=`cat $cur_path/output/0/train_0.log | wc -l`
-num=$(($li - 1))
-train_accuracy=`sed -n '${num}p' $cur_path/output/0/train_0.log | awk '{print $3}'`
-echo "Final train_accuracy is ${train_accuracy}"
-E2E训练端到端时长，直接计算，不需要修改
-echo "E2E training Duration sec: $e2e_time"
+#参数改回
+sed -i "50s|${data_path}|PATH_TO_BE_CONFIGURED|g"  $cur_path/../src/configs/res50_256bs_1p_eval.py
+sed -i "107s|${cur_path}/output/0/d\_solution/ckpt0|PATH_TO_BE_CONFIGURED|g"  $cur_path/../src/configs/res50_256bs_1p_eval.py
 
+#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=`cat ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log | grep "FPS: " | awk -F "FPS: " '{print $2}' | awk -F "  loss:" '{print $1}' | tail -n +2 | awk '{sum+=$1} END {print 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 1 top1 $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $3}'`
+train_accuracy=`grep -A 2 "top1" ${cur_path}/output/0/train_0.log|tail -1|awk 'END {print $3}'`
+#打印，不需要修改
+echo "Final Train Accuracy : ${train_accuracy}"
+echo "E2E Training Duration sec : $e2e_time"
+
+#稳定性精度看护结果汇总
 #训练用例信息，不需要修改
-BatchSize=256
+BatchSize=${batch_size}
 DeviceType=`uname -m`
 if [[ ${fp32} == "--fp32" ]];then
   CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'fp32'_'acc'
@@ -187,26 +169,26 @@ elif [[ ${hf32} == "--hf32" ]];then
 else
   CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc'
 fi
+##获取性能数据
+#吞吐量，不需要修改
+ActualFPS=${FPS}
+#单迭代训练时长，不需要修改
+TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${batch_size}'*'${RANK_SIZE}'*1000/'${FPS}'}'`
 
-
-##获取性能数据，不需要修改
-#吞吐量
-ActualFPS="NULL"
-#单迭代训练时长
-TrainingTime="NULL"
-
-##获取Loss，通过train_*.log中关键字，需要根据模型审视
-#grep train_loss $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|grep -v BatchTimestamp|awk '{print $10}'|sed 's/,//g'|sed '/^$/d' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
+#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
+grep "FPS: " $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "loss: " '{print $2}' | awk -F "total" '{print $1}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
 
 #最后一个迭代loss值，不需要修改
-ActualLoss=`sed -n '${num}p' $cur_path/output/0/train_0.log | awk '{print $5}'`
+ActualLoss=`awk 'END {print}' $cur_path/output/$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 "RANK_SIZE = ${RANK_SIZE}" >> $cur_path/output/$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 "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log