diff --git a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/main.py b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/main.py
index c7bdf20029af5e256afcd2230ba5b28d9e587086..301ce9d5d9b901304eee1ee9f698cd9fe16864f5 100644
--- a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/main.py
+++ b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/main.py
@@ -15,6 +15,8 @@
 import os
 import json
 import torch
+if torch.__version__.startswith('1.8'):
+    import torch_npu
 from torch import nn
 from torch import optim
 from torch.optim import lr_scheduler
@@ -112,11 +114,12 @@ if __name__ == '__main__':
 
     if opt.use_apex == 1:
         from apex import amp
-        if opt.loss_scale < 0:
-            model, optimizer = amp.initialize(model, optimizer, opt_level=opt.opt_level, loss_scale=None)
-        else:
-            # model, optimizer = amp.initialize(model, optimizer, opt_level=opt.opt_level, loss_scale=opt.loss_scale, combine_grad=True)
-            model, optimizer = amp.initialize(model, optimizer, opt_level=opt.opt_level, loss_scale=opt.loss_scale)
+        #if opt.loss_scale < 0:
+        #    model, optimizer = amp.initialize(model, optimizer, opt_level=opt.opt_level, loss_scale=None)
+        #else:
+        #    # model, optimizer = amp.initialize(model, optimizer, opt_level=opt.opt_level, loss_scale=opt.loss_scale, combine_grad=True)
+        #    model, optimizer = amp.initialize(model, optimizer, opt_level=opt.opt_level, loss_scale=opt.loss_scale)
+        model,optimizer = amp.initialize(model, optimizer, opt_level=opt.opt_level,loss_scale=opt.loss_scale)
 
     # resume model
     best_pre1 = 0
diff --git a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/requirements.txt b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/requirements.txt
index 39b92c7dba6bef1d4fcd9ac9db9d29ceb07a0ebe..105f194e33136c1327f2b32520e907ead279fbf5 100644
--- a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/requirements.txt
+++ b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/requirements.txt
@@ -1,4 +1,3 @@
-Python==3.7.0
-torch==1.5.0
+numpy>=1.17.3
 torchvision==0.6.0
 opencv-python==4.5.3.56
diff --git a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/getmodel.py b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/getmodel.py
index 2e6b18ed23a9a1e855193f0f677cbac63791d18e..6a4db1bd282ad538a9cb75ef4e4cc48a166f377e 100644
--- a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/getmodel.py
+++ b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/getmodel.py
@@ -15,6 +15,9 @@
 import os
 import json
 import torch
+if torch.__version__.startswith('1.8'):
+    import torch_npu
+
 from torch import nn
 from torch import optim
 from torch.optim import lr_scheduler
diff --git a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/opts.py b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/opts.py
index e4583c32e613ba471d873a4d9971d3e5455f19c6..ceb6e6d09d71160b65ce30358611c7d69ec17041 100644
--- a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/opts.py
+++ b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/opts.py
@@ -41,7 +41,7 @@ def parse_opts():
     parser.add_argument('--use_prof', default=1, type=int, help='use_prof')
     parser.add_argument('--use_apex', default=0, type=int, help='use_apex')
     parser.add_argument('--opt_level', default='O2', type=str, help='Initial opt_level')
-    parser.add_argument('--loss_scale', default=128, type=float, help='Initial loss_scale')
+    parser.add_argument('--loss_scale', default="dynamic", help='Initial loss_scale')
 
     parser.add_argument('--batch_size', default=8, type=int, help='Batch Size')
     parser.add_argument('--n_epochs', default=2, type=int, help='Number of total epochs to run')
diff --git a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/spatial_transforms.py b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/spatial_transforms.py
index bc532e2093be2256191bdd0f0c4f44e744b24f01..5bb23484f41da97b38581a60b443df2d55f18f30 100644
--- a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/spatial_transforms.py
+++ b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/spatial_transforms.py
@@ -18,6 +18,9 @@ import numbers
 import collections
 import numpy as np
 import torch
+if torch.__version__.startswith('1.8'):
+    import torch_npu
+
 import cv2
 import scipy.ndimage
 from PIL import Image, ImageOps
diff --git a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/test.py b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/test.py
index 4a1abb3f5dc4ca7ed0f7e0c24e89213a6afca50c..b780dd1557c77607d4a9c8b05ab37476b42d15e6 100644
--- a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/test.py
+++ b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/test.py
@@ -19,6 +19,8 @@ import time
 import os
 import sys
 import json
+if torch.__version__.startswith('1.8'):
+    import torch_npu
 
 from run.utils import AverageMeter
 
diff --git a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/train.py b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/train.py
index 312ff580551ef382c2e41618d2d0153b2eba4405..14225a0476736fc1b013e370bf4f422e3966e746 100644
--- a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/train.py
+++ b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/train.py
@@ -17,6 +17,8 @@ from torch.autograd import Variable
 import time
 import os
 import sys
+if torch.__version__.startswith('1.8'):
+    import torch_npu
 
 from run.utils import AverageMeter, calculate_accuracy
 
@@ -126,7 +128,7 @@ def train_epoch(epoch, data_loader, model, criterion, optimizer, opt,
         if i >= 2:
             tot_time.update(current_batch_time)
 
-        if device_ids == 0:  # distributed master or 1p
+        if (device_ids == 0) or (opt.device_num == 1): # distributed master or 1p
             batch_logger.log({
                 'date': time.strftime('%Y%m%d%H%M%S', time.localtime(time.time())),
                 'epoch': epoch,
@@ -159,7 +161,7 @@ def train_epoch(epoch, data_loader, model, criterion, optimizer, opt,
 
     epoch_fps = opt.batch_size * opt.device_num / tot_time.avg
 
-    if device_ids == 0:  # distributed master or 1p
+    if (device_ids == 0)or (opt.device_num == 1):  # distributed master or 1p
         epochlog = {
             'date': time.strftime('%Y%m%d%H%M%S', time.localtime(time.time())),
             'epoch': epoch,
diff --git a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/utils.py b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/utils.py
index 70357d2254782432d3a9d3c66068c79aa3f85442..40c1216f99eae37b70cf4cceeae3ae556eaf378f 100644
--- a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/utils.py
+++ b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/utils.py
@@ -18,6 +18,9 @@ import shutil
 import numpy as np
 import json
 import os
+if torch.__version__.startswith('1.8'):
+    import torch_npu
+
 from run.mean import get_mean, get_std
 
 class AverageMeter(object):
diff --git a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/validation.py b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/validation.py
index 61827e741592b277f61c32a2b470f61d67d3a41e..795e555bc17be55fdcdd5ae288efa1033c6eef22 100644
--- a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/validation.py
+++ b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/run/validation.py
@@ -13,6 +13,9 @@
 # limitations under the License.
 # ============================================================================
 import torch
+if torch.__version__.startswith('1.8'):
+    import torch_npu
+
 from torch.autograd import Variable
 import time
 import sys
diff --git a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/test/npu_train_full_1p.sh b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/test/npu_train_full_1p.sh
index 17b06a389225a6b971d4768dd692b2a5063ff232..8e898916bf1d4f6671b1cb38e6915230b9933b5d 100644
--- a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/test/npu_train_full_1p.sh
+++ b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/test/npu_train_full_1p.sh
@@ -1,6 +1,31 @@
 #!/usr/bin/env bash
+
+source env_npu.sh 
+
+# 网络名称，同目录名称
+Network="Efficient-3DCNNs"
+# 训练batch_size
+batch_size=80
+
 root_path=$1
 
+#################创建日志输出目录，不需要修改#################
+ASCEND_DEVICE_ID=0
+if [ -d ${root_path}/results/${ASCEND_DEVICE_ID} ];then
+    rm -rf ${root_path}/results/${ASCEND_DEVICE_ID}
+    mkdir -p ${root_path}/results/$ASCEND_DEVICE_ID
+else
+    mkdir -p ${root_path}/results/$ASCEND_DEVICE_ID
+fi
+
+#################启动训练脚本#################
+#训练开始时间，不需要修改
+start_time=$(date +%s)
+
+
+KERNEL_NUM=$(($(nproc)/8))
+PID_START=$((KERNEL_NUM * RANK_ID))
+PID_END=$((PID_START + KERNEL_NUM - 1))
 nohup python3.7 ../main.py \
     --root_path ${root_path} \
     --gpu_or_npu npu \
@@ -16,7 +41,64 @@ nohup python3.7 ../main.py \
 	  --batch_size 80 \
 	  --n_threads 16 \
 	  --ft_portion complete \
-	  > ${root_path}/results/npu_train_full_1p.log 2>&1 &
+	  > ${root_path}/results/${ASCEND_DEVICE_ID}/npu_train_full_1p.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 'fps' ${root_path}/results/${ASCEND_DEVICE_ID}/npu_train_full_1p.log| tail -n 1 |awk '{print $6}'|awk 'END {print}'`
+FPS=${FPS%,*}
+# 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
+top1_acc=`grep 'test top1 acc' ${root_path}/results/${ASCEND_DEVICE_ID}/npu_train_full_1p.log|awk '{print $4}'|awk 'END {print}'`
+top1_acc=`echo ${top1_acc%,*}`
+# 打印，不需要修改
+echo "Final Train Accuracy : ${top1_acc}"
+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}_${RANK_SIZE}'p'_'acc'
+
+# 获取性能数据，不需要修改
+# 吞吐量
+ActualFPS=${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 'date' ${root_path}/results/${ASCEND_DEVICE_ID}/npu_train_full_1p.log|awk '{print $8}' >> ${root_path}/results/${ASCEND_DEVICE_ID}/train_${CaseName}_loss.txt
+# 删除loss值后逗号
+sed -i 's/,//g' ${root_path}/results/${ASCEND_DEVICE_ID}/train_${CaseName}_loss.txt
+
+# 最后一个迭代loss值，不需要修改
+ActualLoss=`awk 'END {print}'  ${root_path}/results/${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}" >  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "RankSize = ${RANK_SIZE}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "BatchSize = ${BatchSize}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "DeviceType = ${DeviceType}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "CaseName = ${CaseName}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualFPS = ${ActualFPS}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainingTime = ${TrainingTime}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainAccuracy = ${top1_acc}" >> ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualLoss = ${ActualLoss}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2e_time}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
 
 
 
diff --git a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/test/npu_train_full_8p.sh b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/test/npu_train_full_8p.sh
index cc344cf943cfce8c6a0a58ae25fbc5ec581eee8c..c658476d0cc182790e193206a3f4710207896131 100644
--- a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/test/npu_train_full_8p.sh
+++ b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/test/npu_train_full_8p.sh
@@ -1,8 +1,30 @@
 #!/usr/bin/env bash
-RANK_ID_START=0
+
+source env_npu.sh 
+
+# 网络名称，同目录名称
+Network="Efficient-3DCNNs"
+# 训练batch_size
+batch_size=640
+
 RANK_SIZE=8
 root_path=$1
 
+#################创建日志输出目录，不需要修改#################
+ASCEND_DEVICE_ID=0
+if [ -d ${root_path}/results/${ASCEND_DEVICE_ID} ];then
+    rm -rf ${root_path}/results/${ASCEND_DEVICE_ID}
+    mkdir -p ${root_path}/results/$ASCEND_DEVICE_ID
+else
+    mkdir -p ${root_path}/results/$ASCEND_DEVICE_ID
+fi
+
+#################启动训练脚本#################
+#训练开始时间，不需要修改
+start_time=$(date +%s)
+
+RANK_ID_START=0
+
 for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
 do
 
@@ -25,5 +47,61 @@ nohup taskset -c $PID_START-$PID_END python3.7 ../main.py --local_rank $RANK_ID
     --batch_size 640 \
     --n_threads 64 \
 	  --ft_portion complete \
-	  > ${root_path}/results/npu_train_full_8p.log 2>&1 &
-done
\ No newline at end of file
+	  > ${root_path}/results/${ASCEND_DEVICE_ID}/npu_train_full_8p.log 2>&1 &
+done
+
+
+
+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 'fps' ${root_path}/results/${ASCEND_DEVICE_ID}/npu_train_full_8p.log| tail -n 1 |awk '{print $6}'|awk 'END {print}'`
+FPS=${FPS%,*}
+# 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
+top1_acc=`grep 'test top1 acc' ${root_path}/results/${ASCEND_DEVICE_ID}/npu_train_full_8p.log|awk '{print $4}'|awk 'END {print}'`
+top1_acc=`echo ${top1_acc%,*}`
+# 打印，不需要修改
+echo "Final Train Accuracy : ${top1_acc}"
+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}_${RANK_SIZE}'p'_'acc'
+
+# 获取性能数据，不需要修改
+# 吞吐量
+ActualFPS=${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 'date' ${root_path}/results/${ASCEND_DEVICE_ID}/npu_train_full_8p.log|awk '{print $8}' >> ${root_path}/results/${ASCEND_DEVICE_ID}/train_${CaseName}_loss.txt
+# 删除loss值后逗号
+sed -i 's/,//g' ${root_path}/results/${ASCEND_DEVICE_ID}/train_${CaseName}_loss.txt
+
+# 最后一个迭代loss值，不需要修改
+ActualLoss=`awk 'END {print}'  ${root_path}/results/${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}" >  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "RankSize = ${RANK_SIZE}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "BatchSize = ${BatchSize}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "DeviceType = ${DeviceType}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "CaseName = ${CaseName}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualFPS = ${ActualFPS}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainingTime = ${TrainingTime}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainAccuracy = ${top1_acc}" >> ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualLoss = ${ActualLoss}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2e_time}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
diff --git a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/test/npu_train_performance_1p.sh b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/test/npu_train_performance_1p.sh
index 49773c1153c233ad3bbbf5839abadb11d62a7bc1..881ce6da08c8ada7c00da95c028c27060fd8bd30 100644
--- a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/test/npu_train_performance_1p.sh
+++ b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/test/npu_train_performance_1p.sh
@@ -1,6 +1,31 @@
 #!/usr/bin/env bash
+
+source env_npu.sh 
+
+# 网络名称，同目录名称
+Network="Efficient-3DCNNs"
+# 训练batch_size
+batch_size=80
+
+
 root_path=$1
 
+#################创建日志输出目录，不需要修改#################
+ASCEND_DEVICE_ID=0
+if [ -d ${root_path}/results/${ASCEND_DEVICE_ID} ];then
+    rm -rf ${root_path}/results/${ASCEND_DEVICE_ID}
+    mkdir -p ${root_path}/results/$ASCEND_DEVICE_ID
+else
+    mkdir -p ${root_path}/results/$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)
+
+
+
+
 nohup python3.7 ../main.py \
     --root_path ${root_path} \
     --gpu_or_npu npu \
@@ -16,7 +41,59 @@ nohup python3.7 ../main.py \
 	  --batch_size 80 \
 	  --n_threads 16 \
 	  --ft_portion complete \
-	  > ${root_path}/results/npu_train_performance_1p.log 2>&1 &
+	  > ${root_path}/results/${ASCEND_DEVICE_ID}/npu_train_performance_1p.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 'fps' ${root_path}/results/${ASCEND_DEVICE_ID}/npu_train_performance_1p.log| tail -n 1 |awk '{print $6}'|awk 'END {print}'`
+FPS=${FPS%,*}
+# 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
+top1_acc=`grep 'test top1 acc' ${root_path}/results/${ASCEND_DEVICE_ID}/npu_train_performance_1p.log|awk '{print $4}'|awk 'END {print}'`
+top1_acc=`echo ${top1_acc%,*}`
+# 打印，不需要修改
+echo "Final Train Accuracy : ${top1_acc}"
+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}_${RANK_SIZE}'p'_'acc'
+
+# 获取性能数据，不需要修改
+# 吞吐量
+ActualFPS=${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 'date' ${root_path}/results/${ASCEND_DEVICE_ID}/npu_train_performance_1p.log|awk '{print $8}' >> ${root_path}/results/${ASCEND_DEVICE_ID}/train_${CaseName}_loss.txt
+# 删除loss值后逗号
+sed -i 's/,//g' ${root_path}/results/${ASCEND_DEVICE_ID}/train_${CaseName}_loss.txt
 
+# 最后一个迭代loss值，不需要修改
+ActualLoss=`awk 'END {print}'  ${root_path}/results/${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}" >  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "RankSize = ${RANK_SIZE}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "BatchSize = ${BatchSize}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "DeviceType = ${DeviceType}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "CaseName = ${CaseName}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualFPS = ${ActualFPS}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainingTime = ${TrainingTime}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainAccuracy = ${top1_acc}" >> ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualLoss = ${ActualLoss}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2e_time}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
diff --git a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/test/npu_train_performance_8p.sh b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/test/npu_train_performance_8p.sh
index ad27280e186819eedf1dd9a58383e62a9ba1062c..5df7b95cdee07150895b30c9601309f7d5937653 100644
--- a/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/test/npu_train_performance_8p.sh
+++ b/PyTorch/contrib/cv/classification/Efficient-3DCNNs_ID1230_for_PyTorch/test/npu_train_performance_8p.sh
@@ -1,8 +1,30 @@
 #!/usr/bin/env bash
-RANK_ID_START=0
+
+source env_npu.sh 
+
+# 网络名称，同目录名称
+Network="Efficient-3DCNNs"
+# 训练batch_size
+batch_size=640
+
 RANK_SIZE=8
 root_path=$1
 
+#################创建日志输出目录，不需要修改#################
+ASCEND_DEVICE_ID=0
+if [ -d ${root_path}/results/${ASCEND_DEVICE_ID} ];then
+    rm -rf ${root_path}/results/${ASCEND_DEVICE_ID}
+    mkdir -p ${root_path}/results/$ASCEND_DEVICE_ID
+else
+    mkdir -p ${root_path}/results/$ASCEND_DEVICE_ID
+fi
+
+#################启动训练脚本#################
+#训练开始时间，不需要修改
+start_time=$(date +%s)
+
+RANK_ID_START=0
+
 for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
 do
 
@@ -22,8 +44,62 @@ nohup taskset -c $PID_START-$PID_END python3.7 ../main.py --local_rank $RANK_ID
     --learning_rate 0.04 \
     --droupout_rate 0.2 \
     --n_epochs 2 \
-    --batch_size 640 \
+    --batch_size ${batch_size} \
     --n_threads 64 \
 	  --ft_portion complete \
-	  > ${root_path}/results/npu_train_performance_8p.log 2>&1 &
-done
\ No newline at end of file
+	  > ${root_path}/results/${ASCEND_DEVICE_ID}/npu_train_performance_8p.log 2>&1 &
+done
+
+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 'fps' ${root_path}/results/${ASCEND_DEVICE_ID}/npu_train_performance_8p.log| tail -n 1 |awk '{print $6}'|awk 'END {print}'`
+FPS=${FPS%,*}
+# 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
+top1_acc=`grep 'test top1 acc' ${root_path}/results/${ASCEND_DEVICE_ID}/npu_train_performance_8p.log|awk '{print $4}'|awk 'END {print}'`
+top1_acc=`echo ${top1_acc%,*}`
+# 打印，不需要修改
+echo "Final Train Accuracy : ${top1_acc}"
+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}_${RANK_SIZE}'p'_'acc'
+
+# 获取性能数据，不需要修改
+# 吞吐量
+ActualFPS=${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 'date' ${root_path}/results/${ASCEND_DEVICE_ID}/npu_train_performance_8p.log|awk '{print $8}' >> ${root_path}/results/${ASCEND_DEVICE_ID}/train_${CaseName}_loss.txt
+# 删除loss值后逗号
+sed -i 's/,//g' ${root_path}/results/${ASCEND_DEVICE_ID}/train_${CaseName}_loss.txt
+
+# 最后一个迭代loss值，不需要修改
+ActualLoss=`awk 'END {print}'  ${root_path}/results/${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}" >  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "RankSize = ${RANK_SIZE}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "BatchSize = ${BatchSize}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "DeviceType = ${DeviceType}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "CaseName = ${CaseName}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualFPS = ${ActualFPS}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainingTime = ${TrainingTime}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainAccuracy = ${top1_acc}" >> ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualLoss = ${ActualLoss}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2e_time}" >>  ${root_path}/results/$ASCEND_DEVICE_ID/${CaseName}.log